{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 金融领域中的自然语言处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自然语言处理( Natural Language Processing, NLP)是计算机科学领域与人工智能领域中的一个重要方向。简单来说，处理自然语言的过程就是让机器去理解人的文本或语言，其中如翻译、语音识别、语义理解、智能问答，知识图谱等都属于NLP的范畴。\n",
    "\n",
    "自计算机诞生伊始，人类就致力于让机器来理解我们语言。随着人工智能、计算机科学、信息工程、统计学、甚至语言学等学科知识的不断进步，目前NLP已经拥有了大量的商业应用，如机器翻译（Google翻译、有道翻译等）、知识图谱（以Google为代表的搜索引擎）、智能问答（Apple的Siri、亚马逊的Alexa以及各种智能机器人）等等。\n",
    "\n",
    "但是，金融领域的NLP目前仍处于探索阶段，金融本身是一个专业性很高的领域，很多词汇在金融语境下会产生特殊含义，所有的子问题都会有一个独特的理解方式，而且金融领域衡量处理结果的方式也与其他领域不同。比如针对舆情分析，金融领域要求对市场未来的走势有一定的预见性。\n",
    "\n",
    "因此，金融领域的NLP需要准备特殊的训练数据集，而目前NLP所有方法都是基于大量的数据集基础上，数据集的缺乏也是目前NLP在金融领域所面临的最大问题之一，这也是金融领域高度的专业性与深度导致的。\n",
    "\n",
    "- https://blog.csdn.net/AdamCY888/article/details/130198717"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一、一个强大的NLP系统能够帮助金融机构解决哪些实际问题？\n",
    "\n",
    "全网舆情监控、产业链分析、让机器帮助金融机构阅读大量新闻。\n",
    "\n",
    "例如，商业银行希望使用更全面的数据进行企业的信贷风险管理，提前感知企业的潜在风险。目前常规的风险评估方法是根据企业公布的年报，并综合信贷员实地调查的结果进行判断，但是由于企业自身风险报出通常具有滞后性，公开信息覆盖度不高，看到的往往只是冰山一角，因此判断风险的手段十分单一。这也是NLP与人工智能可以发挥作用的地方。\n",
    "\n",
    "NLP可以对信息进行多维关系的挖掘，评估企业之间的关系，并通过知识图谱直观呈现企业之间的关联，提前设立预警信号，一旦企业关系网内的相关对象出现任意变动，便可根据关系权重，快速地评估对整个关系网的影响程度。\n",
    "\n",
    "![](http://5b0988e595225.cdn.sohucs.com/images/20180817/1be14f4f13914a80bd3c29ed7e74b4c4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二、 金融语义应用场景概念框\n",
    "\n",
    "1. 智能问答和语义搜索\n",
    "\n",
    "智能问答和语义搜索是自然语言处理（NLP）的关键技术，目的是让用户以自然语言形式提出问题，深入进行语义分析，以更好理解用户意图，快速准确获取知识库中的信息。在用户界面上，既可以表现为问答机器人的形式（智能问答），也可以为搜索引擎的形式（语义搜索）。智能问答系统一般包括问句理解、信息检索、答案生成三个环节。基于知识图谱的智能问答相比基于文本的问答更能满足金融业务实际需求。\n",
    "\n",
    "2. 资讯与舆情分析\n",
    "\n",
    "金融资讯信息非常丰富，例如公司新闻（公告、重要事件、财务状况等）、金融产品资料（股票、证券等）、宏观经济（通货膨胀、失业率等）、政策法规（宏观政策、税收政策等）、社交媒体评论等。\n",
    "\n",
    "3. 金融预测和分析\n",
    "\n",
    "基于语义的金融预测即利用金融文本中包含的信息预测各种金融市场波动，它是以NLP等人工智能技术与量化金融技术的结合。\n",
    "\n",
    "4. 文档信息抽取\n",
    "\n",
    "信息抽取是NLP的一种基础技术，是NLP进一步进行数据挖掘分析的基础，也是知识图谱中知识抽取的基础。采用的方法包括基于规则模板的槽填充的方法、基于机器学习或深度学习的方法。按抽取内容分可以分为实体抽取、属性抽取、关系抽取、规则抽取、事件抽取等。\n",
    "\n",
    "5. 自动文档生成\n",
    "\n",
    "自动文档生成指根据一定的数据来源自动产生各类金融文档。常见的需要生成的金融文档如信息披露公告（债券评级、股转书等）、各种研究报告。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、LAC  分词 \n",
    "\n",
    "LAC全称Lexical Analysis of Chinese，是百度自然语言处理部研发的一款联合的词法分析工具，实现中文分词、词性标注、专名识别等功能。\n",
    "\n",
    "代码兼容Python2/3\n",
    "\n",
    "- 全自动安装: ``pip install lac``\n",
    "- 使用百度源安装，安装速率更快：``pip install lac -i https://mirror.baidu.com/pypi/simple``\n",
    "或者使用阿里云 ``http://mirrors.aliyun.com/pypi/simple/``\n",
    "\n",
    "- 代码：https://github.com/baidu/lac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simpleNote: you may need to restart the kernel to use updated packages.\n",
      "Collecting lac\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/c3/88/966e99c95cac93730a7f3cdf92a17e2a0e924bea61b9a86ae7995feaa4fe/LAC-2.1.2.tar.gz (64.8 MB)\n",
      "Collecting paddlepaddle>=1.6\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/1d/7a/86cab456339e7aaba6838960eb304dfa0413024625af2f99eb1626b36d32/paddlepaddle-2.5.2-cp39-cp39-win_amd64.whl (72.0 MB)\n",
      "Collecting opt-einsum==3.3.0\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/bc/19/404708a7e54ad2798907210462fd950c3442ea51acc8790f3da48d2bee8b/opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
      "Collecting httpx\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/a2/65/6940eeb21dcb2953778a6895281c179efd9100463ff08cb6232bb6480da7/httpx-0.25.2-py3-none-any.whl (74 kB)\n",
      "Requirement already satisfied: Pillow in c:\\programdata\\anaconda3\\lib\\site-packages (from paddlepaddle>=1.6->lac) (8.4.0)\n",
      "Requirement already satisfied: decorator in c:\\programdata\\anaconda3\\lib\\site-packages (from paddlepaddle>=1.6->lac) (5.1.0)\n",
      "Collecting astor\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/c3/88/97eef84f48fa04fbd6750e62dcceafba6c63c81b7ac1420856c8dcc0a3f9/astor-0.8.1-py2.py3-none-any.whl (27 kB)\n",
      "Collecting protobuf<=3.20.2,>=3.1.0\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/95/ec/410f21dd62810df692ced49ce7c7777c8d2ad239fdd26fcd72d5c5f42b7e/protobuf-3.20.2-cp39-cp39-win_amd64.whl (904 kB)\n",
      "Requirement already satisfied: numpy>=1.13 in c:\\programdata\\anaconda3\\lib\\site-packages (from paddlepaddle>=1.6->lac) (1.20.3)\n",
      "Requirement already satisfied: idna in c:\\programdata\\anaconda3\\lib\\site-packages (from httpx->paddlepaddle>=1.6->lac) (3.2)\n",
      "Collecting httpcore==1.*\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/56/ba/78b0a99c4da0ff8b0f59defa2f13ca4668189b134bd9840b6202a93d9a0f/httpcore-1.0.2-py3-none-any.whl (76 kB)\n",
      "Requirement already satisfied: anyio in c:\\programdata\\anaconda3\\lib\\site-packages (from httpx->paddlepaddle>=1.6->lac) (2.2.0)\n",
      "Requirement already satisfied: certifi in c:\\programdata\\anaconda3\\lib\\site-packages (from httpx->paddlepaddle>=1.6->lac) (2021.10.8)\n",
      "Requirement already satisfied: sniffio in c:\\programdata\\anaconda3\\lib\\site-packages (from httpx->paddlepaddle>=1.6->lac) (1.2.0)\n",
      "Collecting h11<0.15,>=0.13\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/95/04/ff642e65ad6b90db43e668d70ffb6736436c7ce41fcc549f4e9472234127/h11-0.14.0-py3-none-any.whl (58 kB)\n",
      "Building wheels for collected packages: lac\n",
      "  Building wheel for lac (setup.py): started\n",
      "  Building wheel for lac (setup.py): finished with status 'done'\n",
      "  Created wheel for lac: filename=LAC-2.1.2-py2.py3-none-any.whl size=64814708 sha256=b6a25fb04725c3e2bb6bbb36123753a8ddea53c4b32bed321d5e6995bc12bdf0\n",
      "  Stored in directory: c:\\users\\administrator\\appdata\\local\\pip\\cache\\wheels\\fa\\73\\be\\03e3db8060cdfa5437fe11b67ebd2eb3bcd9c16ab49539375c\n",
      "Successfully built lac\n",
      "Installing collected packages: h11, httpcore, protobuf, opt-einsum, httpx, astor, paddlepaddle, lac\n",
      "Successfully installed astor-0.8.1 h11-0.14.0 httpcore-1.0.2 httpx-0.25.2 lac-2.1.2 opt-einsum-3.3.0 paddlepaddle-2.5.2 protobuf-3.20.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pip install lac -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 其他有帮助的库\n",
    "\n",
    "##### SnowNLP\n",
    "\n",
    "SnowNLP是一个功能强大的中文文本处理库，它囊括了中文分词、词性标注、情感分析、文本分类、关键字/摘要提取、TF/IDF、文本相似度等诸多功能，像隐马尔科夫模型、朴素贝叶斯、TextRank等算法均在这个库中有对应的应用。\n",
    "\n",
    "SnowNLP主要原理是计算出的情感分数表示语义积极的概率，越接近0情感表现越消极，越接近1情感表现越积极。\n",
    "\n",
    "- https://blog.csdn.net/m0_64336780/article/details/127716310"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple\n",
      "Collecting snownlp\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/3d/b3/37567686662100d3bce62d3b0f2adec18ab4b9ff2b61abd7a61c39343c1d/snownlp-0.12.3.tar.gz (37.6 MB)\n",
      "Building wheels for collected packages: snownlp\n",
      "  Building wheel for snownlp (setup.py): started\n",
      "  Building wheel for snownlp (setup.py): finished with status 'done'\n",
      "  Created wheel for snownlp: filename=snownlp-0.12.3-py3-none-any.whl size=37760963 sha256=ea71ff93a7d52df8bbb64c76a83b08f16f8533f12c9acc64553fc0151ec9a5d6\n",
      "  Stored in directory: c:\\users\\administrator\\appdata\\local\\pip\\cache\\wheels\\85\\56\\d8\\8e39fabf48ea2deb370a525b315f23cdc496bae482b61af1e5\n",
      "Successfully built snownlp\n",
      "Installing collected packages: snownlp\n",
      "Successfully installed snownlp-0.12.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install snownlp -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### lxml\n",
    "\n",
    "lxml 是 Python 的第三方解析库，完全使用 Python 语言编写，它对 Xpath 表达式提供了良好的支持，因此能够了高效地解析 HTML/XML 文档。\n",
    "\n",
    "通过 lxml 库解析 HTML 文档。\n",
    "\n",
    "- https://blog.csdn.net/ccc369639963/article/details/123421912"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple\n",
      "Requirement already satisfied: lxml in c:\\programdata\\anaconda3\\lib\\site-packages (4.6.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install lxml -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'LAC是个优秀的分词工具'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from LAC import LAC\n",
    "\n",
    "# 装载分词模型\n",
    "lac = LAC(mode='seg')\n",
    "\n",
    "# 单个样本输入，输入为Unicode编码的字符串\n",
    "text = u\"LAC是个优秀的分词工具\"\n",
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LAC', '是', '个', '优秀', '的', '分词', '工具']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_result = lac.run(text)\n",
    "seg_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LAC是个优秀的分词工具', '百度是一家高科技公司']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 批量样本输入, 输入为多个句子组成的list，平均速率会更快\n",
    "texts = [u\"LAC是个优秀的分词工具\", u\"百度是一家高科技公司\"]\n",
    "texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['LAC', '是', '个', '优秀', '的', '分词', '工具'], ['百度', '是', '一家', '高科技', '公司']]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_result = lac.run(texts)\n",
    "seg_result "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四、结巴分词\n",
    "\n",
    "结巴分词是当前效果较好的一种中文分词器，支持中文简体、中文繁体分词，同时还支持自定义词库。\n",
    "\n",
    "- 参见：https://github.com/fxsjy/jieba  \n",
    "\n",
    "代码对 Python 2/3 均兼容\n",
    "\n",
    "全自动安装：``easy_install jieba`` 或者 ``pip install jieba`` / ``pip3 install jieba``\n",
    "\n",
    "``jieba.cut`` 方法接受四个输入参数: （1）需要分词的字符串；（2）cut_all 参数用来控制是否采用全模式；（3）HMM 参数用来控制是否使用 HMM 模型；（4）use_paddle 参数用来控制是否使用paddle模式下的分词模式，paddle模式采用延迟加载方式，通过enable_paddle接口安装paddlepaddle-tiny，并且import相关代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple\n",
      "Requirement already satisfied: jieba in c:\\programdata\\anaconda3\\lib\\site-packages (0.42.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install jieba -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Dumping model to file cache C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.629 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full Mode: 我/ 来到/ 北京/ 清华/ 清华大学/ 华大/ 大学\n"
     ]
    }
   ],
   "source": [
    "import jieba\n",
    "text = '我来到北京清华大学'\n",
    "    \n",
    "seg_list = jieba.cut(text, cut_all=True)\n",
    "  # cut_all=True：采用全模式。【全模式】: 我/ 来到/ 北京/ 清华/ 清华大学/ 华大/ 大学\n",
    "  # cut_all=False：精确模式。【精确模式】: 我/ 来到/ 北京/ 清华大学\n",
    "print(\"Full Mode: \" + \"/ \".join(seg_list))\n",
    "  # join()函数是 split() 方法的逆方法，用来将列表（或元组）中包含的多个字符串连接成一个字符串。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[pair('我', 'r'), pair('来到', 'v'), pair('北京', 'ns'), pair('清华大学', 'nt')]\n"
     ]
    }
   ],
   "source": [
    "import jieba.posseg as posseg\n",
    "  # 词性标注\n",
    "seg = posseg.lcut(text)\n",
    "  # jieba.cut 以及 jieba.cut_for_search 返回的结构都是一个可迭代的 generator，可以使用 for 循环来获得分词后得到的每一个词语(unicode)，或者用\n",
    "  # jieba.lcut 以及 jieba.lcut_for_search 直接返回 list.\n",
    "print(seg)"
   ]
  },
  {
   "attachments": {
    "1702902840039.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![1702902840039.png](attachment:1702902840039.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 五、关键词提取\n",
    "\n",
    "![](https://upload-images.jianshu.io/upload_images/11482169-b1f9fd2c1fc2bffe.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 基于 TF-IDF 算法的关键词提取\n",
    "\n",
    "（1）简介\n",
    "\n",
    "TF-IDF(Term Frequency-Inverse Document Frequency, 词频-逆文件频率)是一种用于信息检索（information retrieval）与文本挖掘（text mining）的常用加权技术。TF-IDF是一种统计方法，用以评估一个词语对于一个文件集或一个语料库中的一份文件的重要程度，其原理可概括为：\n",
    "\n",
    "> TF-IDF的主要思想是：如果某个单词在一篇文章中出现的频率TF高，并且在其他文章中很少出现，则认为此词或者短语具有很好的类别区分能力，适合用来分类。\n",
    "\n",
    "计算公式：TF-IDF = TF * IDF，其中：\n",
    "\n",
    "- TF(term frequency, TF)：词频，某一个给定的词语在该文件中出现的次数\n",
    "\n",
    "- IDF(inverse document frequency, IDF)：逆文件频率，某一特定词语的IDF，可以由总文件数目除以包含该词语的文件的数目，再将得到的商取对数得到。如果包含词条t的文档越少, IDF越大，则说明词条具有很好的类别区分能力\n",
    "\n",
    "某一特定文件内的高词语频率，以及该词语在整个文件集合中的低文件频率，可以产生出高权重的TF-IDF。因此，TF-IDF倾向于过滤掉常见的词语，保留重要的词语。\n",
    "\n",
    "（2）TF-IDF应用\n",
    "\n",
    "搜索引擎；关键词提取；文本相似性；文本摘要\n",
    "\n",
    "https://zhuanlan.zhihu.com/p/592645536\n",
    "\n",
    "（3）jieba库实现TF-IDF算法主要是通过调用extract_tags函数实现\n",
    "\n",
    "https://blog.csdn.net/m0_64336780/article/details/130876339"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF\n",
      "反转 0.41\n",
      "A股 0.41\n",
      "50% 0.41\n",
      "起点 0.39\n",
      "季度 0.33\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "    # from jieba.analyse import *\n",
    "import jieba.analyse as analyse\n",
    "    # 基于 TF-IDF 算法的关键词抽取\n",
    "\n",
    "with open('sample.txt', encoding='utf-8') as f:\n",
    "    data = f.read()\n",
    "    # 文件的读操作\n",
    "\n",
    "data\n",
    "\n",
    "with open(r'F:\\hongdou\\1111111111111111课程\\Python\\python_course-master\\ProgramSession\\sample.txt', encoding='utf-8') as f:\n",
    "    data = f.read()\n",
    "    # 文件的读操作\n",
    "    # 写路径的时候前面加上r，r\"file\":意思是指为了避免\\xx是一个转义字符而导致的错误，也就是说加上r之后，“”里的就不再出现转义字符，编程纯的文件地址。\n",
    "    # f = open(r\"C:\\Users\\qw\\Desktop\\000000.txt\", 'r', encoding='utf-8',)\n",
    "        \n",
    "print('TF-IDF')\n",
    "for keyword, weight in analyse.extract_tags(data, withWeight=True, topK=5):\n",
    "    print('{} {}'.format(keyword, np.round(weight,2)))\n",
    "    # jieba库实现TF-IDF算法主要是通过调用extract_tags函数实现\n",
    "    # jieba.analyse.extract_tags(data, topK=20, withWeight=False, allowPOS=()):\n",
    "    # （1）data 为待提取的文本\n",
    "    # （2）topK 为返回几个 TF/IDF 权重最大的关键词，默认值为 20\n",
    "    # （3）withWeight 为是否一并返回关键词权重值，默认值为 False\n",
    "    # （4）allowPOS 仅包括指定词性的词，默认值为空，即不筛选"
   ]
  },
  {
   "attachments": {
    "cc99e37ef3794fd097ec721c8adf7809.png": {
     "image/png": 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HGVavXp2VlfXGG2/Y7XaaoeE8KUmSy+Xq2rWrz+fLyckRRVGW5bS0tOTk5Pj4+N69e4d+B61Wq9/vT0lJOXz48L59+8xmc5cuXZKTk0eNGgUVUmmKVsmfn282m1VVdTqdd9999/fff79q1aqkpKTY2FjsD4IQaisYaEAIoZ+pyrnZE999992bb77Jsuxzzz03aNAg7QZRZGTkww8/XFRU9Pnnn3fq1Ck5OVmn08ENeZqhaYI+duzYF198MXXq1HHjxoVdrt13330EQRQXF8+cOTMQCCQkJNx6660TJ07U6/Xn0hwUVZRFmIVrsVj+9Kc/vfXWWzW1NbNnz54+fTrUVGth5adPn/6HP/xhy5YtDz30EAyurtRuungwCYWiKJqh9+3bd+TIkSlTpoQFROAm86YvNgUCgVOnTtXX1+fm5gYCAW3eRGhObwcUCAQ6d+78+OOPnz179tFHH/X5fAMHDJQkCbIwKIqaO3fu/v3733///cuv1wDjELfbbTAYrkRJDr/fn5SU9Oijj9bU1Bw8eNDr9d555528wHfk8h9hB/zTTz9dXl6el5dnNpstFktZWdnKlSt79+7do0cPr88bNg0EocsBk+NKS0vffffdXbt2vfDCC9OmTTMajaIoUiTVqVOnuXPn6nS65cuXcxzXv3//0NeqqlpUVPTJJ5+MHTt26tSp8IggCgbKoNfrhwwZ0r9/f4Zh5s+ff+TIEavVOmvWrNmzZxMhxYm15URGRi5ZsiQrK+vFF18cN27cokWLHA7HuYqnJEVS4ekJUDOiS5cud95559KlS3ft2nXnnXdejf2FELo+dOi7QwghdDVBlIGiKb/f/9abb4mieP/99//mN7+BhAWWZb1eL1TFW7BgQVRUVERExMCBA4mQW7ssy7777rs0Td9xxx1QObzxu0Bswmw2w2xbgiAURVFkRVVUWZG1zhRms/nkyZP5+fmDBw++8847dTqd1WqFNnuhSwt9i5EjR6alpX388cdlZWVXaBddDkmSCIIoLS398MMPDQbD3Llzwya0y5J85syZH374YfPmzZWVlWlpaTRNQ72JJrXTdjQLSnU4HI7c3FxBECIiIvr07QOzowmCEAQhIyNjxIgRpaWlmzdvvszyihRNEQTx/fff//TTT1ei2QF8NGazeffu3aqqGgwGmBpwDVWhT+yUuGTJkm7dukFpfZPJVFxc/O677/p8voiIiLCK/R35uEIdnyRJVqv1tdde271795w5c8aPHw9nA51Ox3IsNNa57bbboPvJ4MGDQ19LkuSnn37qdrunT59OEITP5xMEwWQykSSptTqmKApyo3w+X1VVFRR3DJ1HBsvR6/U+n2/79u2dOnVatGgR1BKiKZqmf65YHHaQQ6xh2LBh6enp69atq6ysvPJ7CyF0vcBAA0Loenfuhg9FkRQpKzJJknl5ecfyj/Xs2XPcuHHac/Q6vVYwPC4uTpKkuLi4pKQkQRAgB4EgiB07dhw6dGjw4ME9e/ZsPAO/uroaLhwjIyOhypff75ckSZZlRVXgXaB2Azz/888/r62pfeihh6BdH7Q9a2FDGIaZMmWK1Wr917/+1QHHgXB1m52dvWfPnlGjRiUmJoZlKIiSGBERsXjx4hdffPEf//jHo48+GhMTE3bXriODw0CW5NLSUr/f37lzZ7vdrqWWwIaMHz++vLx83759WuVLrWbHRb0XLPO777779ttv6+rq2nhLzm+LqqpnzpzhOC4uLi46Ojq0WmdzOsiBJwgCzdDDhw2/7bbbIiIiFEVhGMZut2dlZW3YsEGb06S5hgIoqAMymUx79uzZvXs3z/O33nqr9k2pra1VFAUiAnq9PikpiSCIYcOGESF9KI8dO7Z///6BAwcOGDBAURSdTkeSJAQiIQdBr9dzHGc2mxmG0ev1NTU1FEVp/+mE2blz57fffgsVcGBpYU2RGiNJMiMjY+DAgfn5+Xl5edqDbbJnEELXMww0IITQORRFQWvJb7/9liTJQCAQmkFAM7TBYIiKioIWhklJScnJyVarFa4CfT5fMBhcs2aN3++/9dZb4SVhgxaXyyXJEkVRycnJiqLIslxUVGQ2m6FpGUmRNE0bjUa4gszNzd2yZcvtd9yenp5OEASMlEJzXxvT6XQ33HCD3W7ftm3blRh8XiaGYTwez4EDB+rr6ydNmkScr0mhMRgMDoejU6dOaWlpMA4kSdJut0MYqL1XvyUwZoBQQll52ZkzZ1RVTU1NtVqt2nPg57i4uEGDBh07duzo0aPwuCIrWlnQi31HOGDadkaJllNDEERtbW1+fn55eXlsbGznzp1lWVYV9ec/zeA4DoreX4lUi1aCoR3N0DNmzBg0aFBdXZ3VaoVqkStWrNi9eze0fYHdHraeGHRAF0uW5O+//x4m6SQnJ9tsNngwKirKZrNBBJllWb1eHxcXByc3QTwXQt25c+eRI0fi4+PhXAehSfhSw4lRVVWj0dilSxebzcZx3PGC4xCGgKS50EycmpqaTZs2zZ49e+jQoUajsclgQXNH9YABA+x2++bNm7WlQSIGQghdsg596YYQQlcT3AUVRfHgwYMEQXAcV1JSwrJsaP4q5C9wHAdlt+CFep3eZrNVVFR4vV6apqFsJNHophAkqRIEERMTw3Ec1AvUBjkwnGZZVlXVYDD45ptvJicnw1zcUC0M3mRJTkpKglBFfn5+Ow7zmlNeXl5ZWZmQkACzUTp4+KD1KJqSJRnmhvj9/uLiYlEUe/bsGRERAVUtNZ0SOqWnpxcVFR0/flx7LUVe4JZjY7LSUm7LJRNFUVV+7t1QXFxsNpv1ev2AAQOgdNwFl0CSpNFo9Pl8jaf5tIvY2Nhp06b1yehTV1dHUZTZbOZ5/oUXXqirq7Pb7ZIkeb1eSZIwuIAuB83QBw4cYFmW5/mjR4/CnLiwpDaTycRx3MiRIz0ej3akud3ukydPBgKBwYMHa1k2FNnEF8fpdDIMQ1EUy7FwqmmcNLd06dJAIDB37txL2ISePXvGxsQeOnSIZVn478lsNmNeA0LocrT/RQBCCHVAkZGRJSUl2dnZgiCwHCsIAoQb9Hq9KIpGo/GDDz6AulmiKMIF39GjRwOBQGJiYteuXZtbLEVRoih27twZ7vryPK/dNYJLOlEUSZLcvHnzqVOnFi9efFEz+WmGttvt6enpkiTl5ORoUzA6AkVRJEk6efLk6dOnoRdD61/bwWfO8zx/5syZn3b/VF9fryhKVVXVwYMHnU4npKKEDbYDwUDPnj0rKytLS0t5nocKFDRDd4QNZFlWkqSq6qoDBw6cPXs2GAyWlZUdOXJEFMUBAwaYTCaj0UhSpPanhUV9+eWXt91221dffXXVVr4FI0aMmH7bdKfTyXFcMBiECvyvv/66qqqtmQyC0EX55JNPaJr2eDxhoV5RFF977bXZs2ebzWZRFCFnoby8/NSpUzExMT169CDOn+sanxBUVY2JiWEYRqfTBQIBbUJZaEbDvn37srKyZs+enZiYeAmr3aVLl9i42Orq6t27d5tMJsgJwrgbQuhydOgK3gghdDXp9XqY1NqvX78tW7bo9fr//ve/27ZtmzlzZkZGRkZGBsMwkC8gCALMtvV4PA6HgyAIn8934sQJVVW1dIbmqKoK7Q9JkhQEQRRFraslQRA6na6uru6tt9664447MjMz4Tk6nU6W5VYOinr27Llp06bWZDSEvu+VJsuyLMtlZWWVlZVjxoyxWCxhTd0brxtxPomduIrXuxe7Tz744IP//Oc/0ADSbrdPmjTJ7/c7HI7Y2FhIn4aacBqIBEVERJw9e1YQhJZv+1/ND4ggiK1bt3766aeQxV1fXz9x4sSEhISqqqqEhASbzab1H73gchRFOX78eHV1dXV19VVY7Qvief6ee+45duzY+vXr4atUWlra0NCwbdu2yZMn2+32Dpj7g64tsiRnZmaePn1aVdVDhw7Nnj27X79+w4cPHzt2LDxBVVWKpOB/DVEUtcINdXV1Ho+nd+/eF/ymR0REQBtgjuOarFzz6quv3nTTTVOmTJEkSZtO1crvLDROMpvNNptt3759I0eOJAiC5/mwepMIIXRRMNCAEELhxowZs3nzZoIgSJL0eDzvvfdeRUVFfHz80KFD77nnnh49ephMJpZlTSYTRBkIgjCbzJWVlQ0NDaHT8pvEMEzXrl2tVqskSSzLsiwLk3jht0aj8a233nK5XI8vfpz45VCToihZkiVZau7iLxgMGo3GxMTE+Pj4/Px86OvewppIkqRVFPP7/a3cOWET8gmCaE2nQIqkCJqoq6uDIpo0TWt9HzWhl9phgYaLGgr+HJ5QVIIgoNCmtkx4F2j2RpKkIiuKqmj7GZKW4Xa9VqQ9bPmCIPA87/F4fv/73x8/fvyOO+5YsGCB0+n8+OOP3377bVmWoeqbtltCL/ShDGFcXFxpaenJkyf79+/PcRyUrG9yW6DpaeP1gXQVCN+0fs80Bm0yy8rK3n777bVr106ePHnHjh2xsbFbtmxZsWJFVlYWx3EJCQlxcXGKrISlaofuGb/fz3Gcy+XiOM7r9drtdpvNZrFY6urqoqKiamtrdTqd9mW5yuBb8OSTT5aWlp46dUoQBKfTabPZXnnlle7duycmJiqKEhYPQuiiBIKBCRMmbNq0yWg0wpy7/Pz8NWvWWCyW9PT0Bx98cNCgQZDG5fP54OQPX5+Ghoby8vKBAwc2DjiGnXksFguEySiKys/Pd7lcgiAEg0Gn0ykIwurVq71e75IlS2iaZhgmrK+EIAjayVY7PYY+B6LYKSkpW7duPXbsmCzJJEUaDAZIuboiuwwhdB3AQANCCP0Mrva6dOmycOHCzZs3NzQ01NbWms3mpKQkWZZzc3P37t07b968hQsXhl0X0gxdXV0NvQYu+BbR0dFGoxG6TtTW1rqiXdo8tuzs7P/85z9Lly6lGRouB+HeFKRR6PV67fHGaJqmKMrhcMCwHGaht7AmNE17vV5FUT799NO9e/e2chf93KTj/IyGRx99NDU1Va/XtxDaoGgq4AtAwQKLxdJyBIQICTRA4kOTF8ctryFBnJsdSBMtZYLQDK094VzsRFFD10FbpvYzy7Jut/vpp58+duzY/fff//jjj8uSLErivHnz8vPz169f73K5+vTpA9UHw+4o6nQ6qBTgdrvhtiRFUc1Fjs5tSMiBBjtEVVQYGBuNRqPRCBO2Q9e55SJwvxhgMHRlZeWf/vSnvLy8SZMm/f3vfzeZTIIgTJo46cSJEx999JHZbO7bt6/T6VRUpYU9+dVXX61cuXL69Om333671Wp1uVxwg1Sv1x8+fPjNN99kWXbNmjXNvfwqiI6OXrhw4UsvvVRRUQFfEIPB8Lvf/W7lypVQ4RWnUaBLZrfbU1JSHn300ffffz8YDFqtVpPJ5Pf7TSbTiRMnFixYMGfOnEWLFiUlJdE0rdV6JAgCqjlYrVaLxdLC8mVJTkhI4HmeYRhRFOvr6wmCYBgGJgQdP358w4YNc+fOjYuLa/zaC0acifP/8dlsNpPJ1NDQIMmSjtLhvAmE0GXCQANCCIVLTk6eN29er169PvroI5fLRRBEbW2tJEk2m83r9a5atcpoNC5YsCD0JSzLer1egiDi4+MvuHyXy2W322tqatxud2lpac8ePeHxgwcPvvPOOzNmzBg1apQsyRT9y+n9gcCpU6eio6Ojo6ObXCxDMwRBxMbGyrJMkmRFRUVycnILmfmCIBiNRr1en56e3vob46FRhou62eX1emEXOZ1OmKXS8uyJyxE64UILHEBqg5azAGDcrmU9NC7D1ngbSZJcsWLFTz/9dOONN957772Q20JSpCzLgwcP3rhxo9Vq7du3r9FobHylDrvOZrMVFRWxLEsQBBR4a2ErQtdBCz1wHAexKoqiTCZTC2tLtDjxhOO4995779ChQxRF/fnPfzaZTDqdTlEUn883cODAtWvXKoqSmZnpdDohnNHcJ15TUxMREfHhhx8eOX53YFUAACAASURBVHJk8eLFsGPtdvv27dtXrFhRXl6enp5+lWeCaCAtnGXZAQMGjB49et26dVD9UVGU8vLyZcuW/f73v9frfo71dPxbuKEfKI4Gr46wHIHGTzCZTDNnzkxOTv7000+9Xm9lZSWc5RoaGiwWy9q1a7t27Tp37lxIIiNJUpZkQRQaGhpUVXU4HE6ns6V3p0i73W632SEprKysjDgfHdAxuvXr11ut1hEjRjT5WlEUDQZDWVmZz+eDShDNPS0qKspqtVZWVkLiFcdxOHUCIXQ5MNCAEEK/AMMhq9U6fPjwkSNHFhYWnj179uzZsz/++GN+fr7BYGBZduPGjWGBhpqaGpqmbTZbK3sNQq0HyGjQHty+fTvP89OnT4dJttrdabiu/fe//7169eply5Y1F2iAzHYto6G2tpbneb1O37g4+bnn0zRc9Q4YMGDUqFGtWe2WtXzfDOaJEAShtZy4QnPjtdaeYfUdQu/2a3kZ0FpSe05UZBSEDCiSCgv0aOPkPXv27Nq1i2GYOXPmhA4PGIapra2FmRFwf7LJoTVJkmEZDc1tCEmScHj8Im6iqgRByLJsNpvr6+vNZnNJSQmEw7R3JM83BNXiQVAworGcnJxdu3bp9fo5c+bExsZCGxSfz2e1WCsrKyG45nA4YIZ54+CX5qEHH+rRo8fXX3+dl5f32GOPjR07lmXZdevW7d27Nykp6e677544cWJ7BRrgMBMEweFwLFy4cP/+/RUVFRRFwdylb775JjMzc/r06TCLpAPWa9i/f3/YI6HBhQ64wr9igwcPDv2ndkhDuhnHcf379x8yZEhlZeWRI0cOHDhQUVFRWVlZUFBgMpmWLl06Y8aMqKgoeK2iKjzP+3w+RVEu2N+BoiiO4+IT4guOF5AkqZ3ieJ7PysoqLCycP3++dhIIY7VaOY574403vv766/3792vT5cJAZNBoNNbU1EDc+dy8Lbqjx90QQh0WBhoQQugX4GaOLMkwskrtmtqtWzeCICZNmrRly5aVK1dCXsOpU6dSU1O1V9E0zfM8tARrzbvExMTU1tZSFFVXV0cztMfjKSoq2rZt2x//+Mf09HS4q6xNzldV1efzcRzXr1+/5kaMGlEUtYwDSINvLuMdYiJwP424jBHLxWYlQKPEi02IaL2IiIhPPvlk//79cXFxod0lm7v3G7oagwYNmjJliqqqtC58p0GRgtLS0m3btpWVlaWkpKSkpIQmHXg8nt27dzMMExsbC8dGk3tGVVWGYcxmM2RNEwQRNoAPXZ/s7OyCggK32x0ZGQn50iAqKopl2eLiYpZlP/jgA5qmYagTOlOjoaFhxowZN998c1gBCJ/Ppx1FOTk5sNghQ4YossLQjKqoVotVlMTDhw/X1tbedNNNkZGRFEURFMHzvJ5u+g6noiqjR48ePXp0dnb2Z5999u9//9toNO7Zs2f27NlTp07VauC1S8NLyPSx2+2qqlqt1r///e+vvvrqgQMHSJKUZVmSpHXr1g0aNAhykTpCS84wy5Ytq66u1ul0sCFESNkRAjMarjySJOvr651OZ48ePRoHGlRVhbbHer1ex+ga3A3R0dFxcXFxcXG33HILQRA//fTTyy+/XFpaCpHlqKgomMtAkZQkSYqiQNkFyFFqYTUMBkOnTp1sNhv8ZwEPFhcXr1mzpm/fvjfffHMLLy8tLXU4HCNHjoRzS+NjhjzfXJmiKJiNhT1ZEEKXDwMNCCF0Tk1NzebNm++8887QO/NaOkBcXNz8+fP/97//FRYW6vX6sJxSvV4Pt8d5nm/Ne8XGxh4+fFiSpEAg4Pf7WZZdvnz5uHHjRowYAUuGoZ2qqoqsyIpsMBjmzZsXCARgzNYyKGoAW9HCYF4rSeAP+B0Ox9UfYl25+9vJycmyLDscjoaGBu3B1gQaYJJzk88kKVJRFI/Hk5+fT9N0ZmZmTExM6BMEQThy5EhERERqaqo2tm8cvrmokWFERETnzp2joqJsNlvoRw9F2kpKSkiSHDhwIEMzvMAb9AaSIrWpIjW1NYmJiY3LTGqHbmVlZWFhoSAIPXv0DGuJJwhCYWGhqqp9+vSJjIyEB1s4Qnie1+l0Bw8erK2tVRQFEjpomq6qqiooKLDb7SaTqb2mJISWNdHr9WlpaRMnTjxy5AjHcTRNi6KYlpZmMBg6YIgBZGRk1NXVKYqinZdCDyoMNFxpNE37/X6z2dypU6ewX8Ex88knn0yaNMlisdAM3Xi036NHjx49epSWltbU1ED+AgSMYPYWEfJptpzyQ5JkVFQUTdMWi8Xj9sCDu3fvFgThlltuaTnA3alTpzlz5hiNRgguN36XxmEFCKAoqkIRHfR7gRDq+DDQgBBC5+Tm5n766aezZ88OvXaHhGqv1wvlDODWcWJiYlhmgclkYmhGkiSO41rzXikpKYFAgCTJhoYGmqY3btzIsuykSZMMBgPP8zzPw2x5kiQlWeI4zuFwREdHJyQkEK0bWkiSBDUCWpjKAW0sFFVpr3YAV4goigMGDBg6dKjBYGjNbPbQy+76+npVUSFSE9YFA+5bVlVVQdfGtLS0sKFpfn6+2+1OTEzs3r07PAJDiAsOsCFvpUmjR49WFAX6g4Qth2XZoqKiYDB491130wwdVvJNW39VVUVRpClaC5lpTztx4kRJSQlBED169gibIg4NGqKionr37g33YBmaaS7pmiCIqqqqZcuWFRQUFBcXd+vWbcaMGV988cW0adO2b9+ek5PTpUuXCRMmTJ06teX9cBXwPG80GqdNm1ZfX//GG29QFDV58uQnn3xS27ftNb+jBQ8//LDf7w8Gg9o5B2s0XDVwqx+qyTSZMlBcXLx27dpRo0bBl7TxgN/hcDgcDpIko6OjbTYbRVGiLIZmmbU+lSwhIQEaT/gDfq/Xe+zYsTVr1vz2t7/t379/yy8URTElJQVOa6Gbpv2MRxFC6ErAQANC6HqnDS2OHj3a0NAA3RN0zLkxFUVTqqrq9fpgMBgIBIqKikiSTE9PD2voQNM0RVOKorQyoyEmJkYQBJqmA4HAsWPHvv766/Hjx2dmZvI8D7cutQCBLMuCIKxcuXLAgAEDBgxoYZ68BkrxQcyihXxaSNatLKv84osvWt91orFnnnkGZpdcIU3GSlooJGkwGGAszbLsL8beSjMX0yGLiYqKkiUZbnSHJq3AKEJRFLfbLYqi0+mMjY11uVyhF+i5ubkul8tgMECmA4wfwiY5y9LFdaOEzWyyJJvJZIKyIKHNLzXazVJVURu/HI55qMIAKyyKonbMEwRx6tSpYDCYlpYWGRlJkqQoirIsm5lm75pu2rRp+/btBEE8/NDDo0aP+vrrr1mWHTdu3LRp05YvX75z5878/Pzp06Zf1LZfCTRNK4oSDAah5crNN9+8aNEigiBMRhNBEBDT6VAF8BRFMRlNZrMZ6rbAg6FHcug0CtTmKJLSInSKooTGoYLBoMlkOnDgQE1NDcdxsixDT5mwJdTX18PspD59+kBPIkj/IUkyNImg5QgX/DYyMlKn03k8HlVVGxoaNm7c6HK5Ro8erdPpICDe5GuhSW3//v0jIiKaK6OjyErjcGcHDLohhK4tGGhACF3vYAypququXbsEQYBsAphVoBUOpCjK6XSeOHECbpLfddddYReUMGCz2+1ama6Wde/eXVXVyMjImpqa5cuXR0VF3XTTTURT9RTz8vI2bNjgcDg+//zzRx55ZMKECaF3wBpfCNbU1MAPnTp1annSL/zW5XINGzYMLnnJX2rNhhAEoZU3a1lcXJzT6VQUpaKiIvQGcui2QPkAVVWhkwLP86qq6nQ6uC6H9gHBYJDneYvFcsEBYWg7BoIgWpkCDHGcsIX7/f6IiAij0QjFIwmCiImJCU0iYFk2NzfXZDI5nc4+GX1kSX7qd0+NGzdu9OjRBubnz1SSJYqiCgsLu3Tpot38bG5vQxpF6D+1n+H5ULNNlmVRFCG61DjcQNJk4wGDdmB7PB6fz5eammqz2YLBoNlshv4px48fVxQlIiIiIiKCIIgXX3wxOTl5zpw5zSW/TJkypaGhYdasWWlpaSRJut3u6OholmV79ez1zDPP9O/fv5XfiytKVVUYDR48eHD16tVpaWl/+ctfOnfu/HP2h6LSVIeblw6xztBwW+iYsOXurYggCGgro+XjtD7YB/tZy0sK+55CCPjgwYNerxdCujzP6/V6aEOjyEqQDZpNZkmSqqqq/H7/vHnzWJY1m80Q1SJJUq/Xu1wukiRLS0tFUYT/d35+91/2uVBVNTU1NRAIQMme119/vaio6G9/+xuc5ZqMPldWVn788ccsywqC8Pbbby9btiwtLa3JLYV+lpWVlVVVVcnJyVFRUfA/IE3RGG5ACF0yDDQghBChquqZM2cURREEYe/evSNGjAi7ptTr9W63+5VXXgkEApMnT+7SpUvYHXWaprt27Xry5Mny8vLWvGNkZCTHcTzPnzp1ymAw/OUvf+nVq1fYc0RR5DguNzf37rvvDgaD2dnZRUVFbrc7LJkidCtIkjx79qwoina7He6rX7DGmE6n69WrV0bvDJjer6iKel5rNqSVl6FwVR0XF2cymcrKyiBkEPYcRVFgeE+SJM/zMIUebkHzPO/xeGCUS1GU1pLjSmhyc2C8rShKTEwMwzBxcXExMTGhsY99+/bt3r07Li6ud+/eNEMfO3astLTUarVCbdHQhXu9XpZlY2NjYZkXuyaNad00LrZEfHx8vMvlEkURDhVRFAmCoGk6GAx++eWXoij26NEjPT3d7XafPHHytttuayGyExcX99JLL2kHW3l5Oc/zVVVVJEXGxMTMmTPnyjUZaSUoR2I0Gn/66aeXXnrJ5/O98sornTt3FgQBxvCKrFA01dHGVB1tfa5RFPnzZCiKphS5DQ5Fg8Egy3IgEPB6vXl5ed26dYNvOhS7FSXRaDTSDL1169a9e/eOGjXqhhtuMOgNHMepqmpiTKqqGo3Gzp07O53OiooK6EHTApIkjUaj1WqVZdlqtR4+fHj06NG9e/eGGETYcQInz61bt0ZFRd1///1bt27duXPn0aNHmws0wMuhd2bXrl3hlKIVH0UIoUuDJV4QQtc7uAY9dOhQSUmJXq9ftmyZx+PRfqsNaLdu3ZqTk2Oz2Z577rmoqCgYlWlMJlPfvn0NBsPJkydb86Y2mw0WQlHUhAkThgwZ0vhCUxTF3Nzc0tLSvn37FhUVeb3exMTEFtqtwyrl5+dzHJeenq4N2pt7vpYJrNPpaIamKIpmaJ1Op9frDa1mMplaDmRoDAZDSkqK1Wo9ceKE1+slGt1alGXZYDAEg0HYFQ6Hw+/3wwCV53mIMsCdQ5jq3Jo3bVsURcXExMTHx9fX11dXV+v1ep1O53a7z549++yzz0LifWRkpKqqWVlZSUlJXbt2DSuxptfra2trA4FAfHw8VNwgLnswCR8iTdMkRbYm/qIdaWlpaXFxcXBDlSAIh8MhS3IgEHjqyac8Hg9N02azmabpXbt2WayWnj17tvBB2+127aaucp7b7YZp4Y1rXrQLRVUKCgqef/75ioqKp556asSIEQRBMAzD87wgCB0wytBYx1/DJl25FjOteneKJClSkRUIX4aeuuFXjf+0ZrE6na6oqKiysjI2NnblypWlpaU+ny8YDLIsC0EBnU63Y8eObdu2xcTEPPDAAxEREVBLBXIQFFmhKKpz585xcXGnTp2CZLrm3gu+12azGU6D5eXlLpcLaos0+c0yGo1VVVU5OTn3338/QRBlZWUVFRUtfH/1er2qqGVlZYIg9O/fH+rU0Azdyl2BEEJNwmglQuh6B3fyz5w5Q5Jknz59iouLFy5c+Mgjj2RmZkIDvGAw+MYbb+Tk5HTu3Pmtt96C0VTYHAeSJLt27cowTEVFBTzS8shKUZS0tLTjx4+npaXNmjULbm6HLZYkydTU1AcffLCmpmbfvn02m61fv37aDdjGIJG+uLhYEASoRyiKIiyw8crA7H2COjd0gcEnSZIUSZHUxU2daL24uDij0VhWVhYMBqOiosJKmsOg/fPPPy8vL/f7/dXV1ZWVlRzHxcTE/OEPf+jcubNOp+vbt+/w4cOTk5NbKEx4JYiiCLO1u3btOu+eeb9/5veff/55SkpKYmLiN998889//nP48OEnT54sKyurr68vKiras2fPzJkzG5epJwjC6/VyHBcVFaV1c7h8YXXdWv7stMyC2NjYiRMnnj17du3atampqenp6fn5+f/4xz+sNuuAAQN++umnioqKU6dOff3110OGDGm5bYTWMhMmudxyyy0sy2ZmZhIEwXGcyWTy+/0X7Mx6hWjdVSoqKt54442qqqqHH3749ttvh8xw6CwI5fraZfWuH1BagmZoWZIvfwTb+hyZc/OMVJnjOEEQIiPa7HuXn59fXV2dkZFRVlb229/+9s477xw/fryiKLGxsWVlZT/88MM777xjNBpfeeWVlJQUj8djtVghAcpsNiuqQhO0Xq93Op1Hjx6VJAlmXjT5RlpE2GKxmEwmk8k0f/58ovkvu8/nc7vdv//97wmCkCW5oaGBYZgePXq0sC2KqjQ0NMiyHNq2GSGELgcGGhBC1zuKomRF3r9//6hRo15//fV9+/atWLHij3/8I+TtMwxDkqTVYp07d+69994LZbf0en3jrgSZmZmRkZEnT548efJkWloa3Ddr7sJRlmWXy1VQUDBy5MguXboQBAGLDX2OwWCApoNffvnlwYMHx4wZY7fb9Xq9dnkddomp1+kJgti/fz/P8zNmzBBFEeYdEOen+Ia+hKIohVC0xgRQpSJ0sWHPv0wwb3n48OG9evX68ccfWZYlCKJxn3ZJkkpLS3fs2AGN3Hr37q3N4zh79ixBENXV1TB8hQW2ybq1BlSCJAjCYDDcdfddJEWuW7fukUceEQTBarE+9thjEyZMKCoqev7557/99tutW7c+/PDDY8aMIQhCEITQj7Wurm7Hjh1ms7lLly6NG0+2rPFn4fV64TikSEpVVK1h3gW2JaTo4+zZsx0Ox9q1axcvXkwQhMvlmjp16u23315TU/OnP/3p0KFDDz300MCBA6dPnx62IWG0IAJkmowdO3bs2LHwCHQ/udiNbUOiKDIMI4jCSy+9dOjQoVmzZs2fPx8ehJal7dLbtZXCblmTJPnjjz++/fbbr732WpNhLFVRQ4sCwthVS4yHsCPP82az2e/3u1wueFpzp5TLWe1z6xNadOD88QmTF3iBLygoePnll++7776JEyfCvBtRFHmeh3MvTdGCKGgzFLQpY6qiQkkC+NRYlqUoCgoxQqwWwl6hbSM5jjPoDVlZWZ98/Mn//fH/evfuDbf3m+vfceDggbfffvupp57q1bMXxESazBXyeDzR0dF/+9vfWJZ98803P/zwwzfeeMPlcjmdTlmWq6urhw0bNnPmzCFDhhAEAS1+tPeFTLrU1NQBAwbk5eUdPXp04sSJYSfhMH6/PyMj4/Dhw5mZmZMnTybOB9Eav8Rms2VkZMDPbo/7wIEDv/nNb6DpbHMOHTpUVVWVmJjYq1ev0EMIA3AIoUuGgQaE0PVOURRBFMaPHz99+nSCIJKTk997773s7Gy3211dXe12u/v166cVDG/uihyu9vr06VNQUPDtt9+mpaVBW8HmxmYWi+WOO+4Y8ZsRY8eNNRqN0EuiuXtThYWFpaWlN998s9Vi9Xq9LYzZdu/eDbekkpKSYBh/rkA91UT44GoOrrSgQK9evb777rvdu3endk2VFTksMSE6Ovqpp576wx/+oK0bpBJopc58Pp/dbteqRV5Noas0ffr08ePHFxcXK4rSo0cPvU5PM7TL5Vq1atWpU6eMRmNCQgJ0Qg07ABRFKSkpSU1NHThw4OWsDNSGYBgmOjr6XGXH1kUZCIIIq04/efLkiRMnFhQUQN/W6OhoVVWTkpI+/vjjEydO8DzfvXt3+Jg8Hk9ofZDWvF1HaJsnSVJtbe3KlSsLCwtvueWWJ554QqfThc1Uaq4I3zVHq1/IcRwk8Hs8nrKyspMnT0JXHYIgIiIioqOjBw4cyLIsnNbatoKGNvrVEqPgn/ANgrgJSZH5+flPP/30vHnzJk6cSJyPyVZUVASDQYqiKIqC+qZQEZZhGO3YgylCJElyHAe9Zv1+P0y8omma5/nExMRgMBg6U8BsNsPXtrKy8oUXXnjyySdvvPFGLQSjrfZFbSbLsjExMc8//zy83euvv37y5Mndu3f7/X6fzxcbG9unT5+hQ4fCkyHKQJGU9l5aTkdaWlpiYmJ+fv7QoUNhZkRzR6DBYBg4cKDBYLjnnntav57Hjx8vKiqaMmUKfLW1x0PfheO4vLy8QCAwZswYmJ3X+jI9CCHUHAw0IISud6qi0jQ9adIkm80miqLVatXpdLfccgtxvuqBqqqCIPh8PoPBAJnhTS6HJMlJkyZlZ2d/8/U39957r9FobOHanSTJ0aNHw/235qIMqqIqhFJZWbl3795hw4b16tXrbMnZvLw8CIg0fj7N0J9++mlDQ8Ojjz76iyhDoyaL7UVV1REjRnz99ddbtmyZNm0ahAy033q9XovZEtYijqbp0GZ+MNho36EgjLqhiCb87Ha7nU6n0+mUJVkr6tlkdbesrKzS0tIFCxZo9xsvcx0CgUDYLrpYLMvKspyeng7ZOhzH6RgdNE1MT09nWVan03EcB5U7Lmed2wtN0+vXr1+3bt1NN9309NNPw2AvNDPoGhU6hg9DURQ0ENm7d+/WrVvz8vKio6MjIiJgtk5dXV1ubu7q1avHjBkzceLEPn36mEwmiDVczsA79N2bXNXQn3NycpYuXTpy5Mjbb7+dOB848/l8n3322a5du1RVhTIfOp3OaDRSFCVJ0tq1a7WFQKUDSO7w+Xxw0uZ5Xpblvn37Pvvss42nVsEjDz744MGDB//617++9tpr6enpocEILQ7Smm2EUo4TJ06E+iPBYDA6OrpPnz59+vThOM5gMGibzPM8lMIlpKYXNWrUqA0bNmzfvn3q1KnNTeGRZRmyNoYOHTpq1CiWZc/Fa8gLl2U5W3y2qqqqe/fusKOafA5FUQUFBSUlJfAfX2jTJYQQumQYaEAIXdcgX5cmaOjRCJ0UtY4ScG0KiQkthBiI8/UR+mf2HzNmzMaNG48cOXLDDTe0PDCDAgqqqjaXy0AztCiK9fX1x48fv//++6Ojo997770bbrhBe0LYtWBlZeXJkydTUlIgaV+WZe0GWruDQZ0sy927dx85cuTGjRuLi4vT0tJCL/ShJiVBEH6/X7sgJkmSpMnQq3ZZluH+5FUu0xCGoiifz+dwOHieh3uAsEqyJAuiQNM03KENS2r4/PPPO3fuPHny5Mtff0VRJk2adG5qTKvTGRoLzQ2hGfpcUjpx7piE38LH1Djb/JqwdevWjRs33nDDDU888YTdbg8Gg7RMX7A3asvacRgGNVx/kfOvqCRFyrIM4UVZkmmGPn369Pbt29esWQOVOMaOHRvacSAvL+/TTz/97LPPCgsL77vvvuHDh0Os4XJW7Ocuoaq6bdu2+Pj41NTUJvuh8jy/fv16nucnT55ssVi0aVBGo3HYsGEpKSkFBQU5OTmFhYWCIMyYMWPYsGFhRx10guzSpct99923atWq06dPx8XFjRw5MioqqkuXLhaLBbIhQtcN2hiTJLlkyZLHH3/8nXfeef755+Pi4ohfpmC0cmNJkqyvrzebzSaTiWXZ6OhogiAURYGIgLYcWZJJkoSqOlAMqPGiLBbL4MGD165dm5eX11xXCA3MLoHd5ff7W54NAYrPFjscjpSUFOgcHLpPtPU8fPhwXl7eggULIBcPowwIoTaBgQaE0HWtyULcYRfc5y4TW7zwgrvKiqrcddddO3fuXLp06apVq1RVbSHWAOPMlher0+kMBkNsbKwkSevWrWMYZtiwYfArqG8nKRLDMHBXbf369bW1tS+++GJkZKTWv01L0A29kg67qm6udHkbIklSkRWKpCwWy6233pqVlbV69ernnnsOuj8qihIaXDCbzaEvDF2Otj/bN8oAYBAVtko0Q5uYn4fu2oBWUZQdO3YUFBTMmzcvMTHx8tefoqgbb7wRltyGn90FF3VR79WOw5VgMCjLcm5u7ooVK6xW6zPPPAODKC2wcjnrpsiKFhTbt29fUlLSlSuhF5ZloPn5txQhSRJBEJIk6fV6QRKOHDzy3nvv7dq1a+TIkXfccceNN94YtrEZGRlOp7Oqqio7O1tRlMTExB49eoS17L3Y/aPISoAN2Gy2FStWbNiwYcKECd26dWv8NI7jtmzZ8t13382fP79Xr16hdWfhjr0syX379i0sLISmJ/fdd1+/fv2IX0a4PB4P1N/t2rVrVlaWoijz5s0bO3as1pSn8QkNaiIQBJGSkjJlypQPPvhg3759Y8eOhWivFqZpDVgyxKaJkPMVzPj4xZvS56Z9nTvlUmToEoDJZFqwYEFhYeG///3vkSNHOp1OVVWhDJD2zNACwFo9ndAogyRJ8CrifP0aSJerra09fPjw0KFDY2Ji3G53aNyHJEl4pqIoGzduFEVx9OjRLpcLowwIobbSUW52IYTQNQ2agVEklZSU9Nhjjx09evSjjz4iQmo6XAK4muzWrdsDDzzAMIwsyXfddZfV8nPuK0mRer2+oaEBZthu3rx58ODBQ4cOJUkS5gN3qOZkWt+4jIyM6dOn79ix45tvvjnXFENRLWbLr/vqtra2dsXyFT169Ljrrrsu83Y6ag2dTldYWPjcc88Zjcb/+7//69atW1g71csEX88TJ0688847FEX5/X6O49pw+c296bkeGiHFYiiKkmVZr9fDcXX48OGlS5fu3r170qRJTzzxxIjfjGjcMUSn06WkpNx+++0WiyU/P/+zzz4jLrtoC1Q6LCsry83NPXv2LMMwTSa/+P3+nJyc5OTk/v37Q13SxtFes9kMo3eapt1uueiB5wAAIABJREFUd+MPzuFw2O12WZKPHTu2b9++SZMmzZw5s7l2PECr86LT6QYOHJiUlPTFF1/4fD7i/Ia34/nH4XDcf//9JEl+8MEH0GA4bOLVBdcNXkUQBITSWJaFU2thYWF+fn58fLzVam182oGg25dffrlnz5558+ZBQAchhNoKBhoQQqjNQKzhlltumTVr1tatW/fs2QN3Gi+NIiuKopjN5unTp0+dOnXmrJmJiYlwH5U8z+PxREVFQSqyy+V65plnGIbx+XwdLcpAhFSGUxRl0aJFI0eO/PDDD/Pz83mepxmaZuhrLie/lTweD0EQa9asYTn2hRdecLlcHMe14cb+ugM0l4bn+ePHj//tr3/T6/X33HPPjTfeGFp+rw2WL/CiKPp8vuXLl8Og3Wq1NpkedeWE5gJod+PLy8vff//9ffv2ORyORYsWpaamwhmjydSq4cOHx8fHS5IEHQearCrSetC8pqSkZP/+/bIsd+rUqcm0nb179/7vf/8bNmzYwIEDw+qSntsuirTZbFCNhWGYqqoqSZbCIrYQeqAZeunSpQkJCbNmzSJCWp+0TBTFbt26DRgwIDc39/Dhw/Bg+2ZIKYrSP7P/rFmzdu/enZeXdwlLEASBZVktuUmbRFNeXl5XV9evXz+n09nkiaK0tPSTTz7p0aPHHXfcYTKZIPKCEEJtAgMNCCHUlmiGNplMjzzyyODBg1etWuX1euES+RIGliRFBgIBuKSOi4vjOI7jOFEUQ68X4XJ8zZo1BfkFixcvjo2NhfJpHS3KEAruHz799NOxsbF/+9vfYBzetnebOxSrxbpy5cqsrKzFixe7XC6ouqfNbUFXAs/zr776all52R133DFlyhTIAgidPH+ZjEajXq//y1/+cuzYsQkTJsiSzPN8myz50sDN6pqamrVr13799dc8zz/33HMxMTHwW0VRRFFs3MrR6XSazWa9Xl9WVnbq1KnWJNq0fB4TRbG2tpbjuM6dO3fp0iW0kSq8try8/PDhw4qidO/eXYvLQLNbDUmSFosFbrYzDOPxeGRZDqtuAI0n1q5de/r06QcffDAqKgoK97aGTqdzOp19+/YNBoPff/99+w6ttQZANEPPnDnz1ltvfeaZZ86cOXOxSU8QKKEoyuv1btiwYcqUKXl5eaIk/vTTTyNGjIA2yY0/O4/Hs2zZMqvVunDhQrvdrihKa4o+IIRQK2GgASGELiD0zmFrCIJgtVofeeSRYcOG5eTkQHf3SxhYkiRptVrhph+UkbfZbGE1KTmOO3HihNfrffZPzw4cOBAGVAzDkM1oeRtb//xL2BbtB7jedblcK1as6NevX15eniiKNENr9SN+ZbfovT7v7t2733zzzfHjx5tMJlEUL6q+/QU199ldic+xI+M4Dto3iqL47LPPHjt2bPr06ffeey9MHGjlvABVVWVJFs8TBEEURVmSIVyoRRMoinr22Wd/+OEHh8MBhfrbtyWHz+djWfbIkSP/+c9/bDbb8OHDu3btGlpRtclatjCwhKz7/Px8OCBhAoh2cDZOcwgd0geDQfiB4zh45p49e4LBYK9evex2e1jCgiIroiju2bMnPj5eK3Cgqqpep9cqXMLXn6KoyMhISZJIkjxz5oyiKBRNhdanoCiqoqJiw4YNv/nNb6A+butTEhRFEQQhISEhPj6+uLi4oqLC6/USzX2PLi9i2/L3MewT0ev1CxcunDp16rZt27QHm2wz2eQyoS6D3W5/5513/H5/cXHx//73v+zs7BkzZmRmZhIEYdAbwhZ15swZmqZff/31Pn36UOddz+cQhFDbwmKQCCHUxvR6fV1dnc1me/DBB1mWhe4SsiSHXuS11XWbIAjp6elPP/00cY20A9A2HEYLDz74IMMwOp1OFMXmJnVf63Q63T//+U/4GfqAUAwFt2ThQbyIbxOCIBiNRq/Xu2zZstzc3EGDBj399NMtt1psjCRJgiJo4pcjZEURBIGiKIPBIIpiQ0PD4sWLCwoKVFXt2rVrXGxck1MAriabzeb1ev/73//yPG+xWMaMGZMQn6D9tvEmkyQJxQLr6+tZliVJkuM4eJokSYIg7N271+v1pqamQl8GLU4BMQttOWazGZZjNBp5nj916lRDQ4Ner4+OjrZaraqqQrVXeLIkSyzL5ufnDxo0KDY2Fh6EKS2N1xCmosiyXFtbC9GHsJPDhg0bvF7vnXfeGVoAsjX7SpZlnU7ncDhiYmLy8vLcbneTRSvby4MPPkhc0slc29WxsbE+ny87Ozs/P/+5556DHpxaN5zQj69v376ZmZlXobYIQuj6hIEGhBBqY6qqRkVFKYoSDAbNZnMwGNTpdG07l0G7Lnc4HDBEb8OFXyFhYwn4p9VqlWVZlmStJvyvb9St3Vj2+/0mowmOhF/fZrY7vV5PkuTatWvXrl3bq1ev5cuX8zzP0MxFRQHO11r8uUeALMuSJImiaDFbjhw5sm3bth07dpSVlZEkGRcXd/PNN7d7lIEgCJZlDxw4cPjwYZPJZLfbhw4desG1gu+d2+2GpoyKosAM//r6+tdee+3QoUOqqp45c+bPf/7zggULFFnRuvD++OOPL7/8st1uf+211+Li4nQ63WOPPZaVlaXX62maNhgMcXFxmzdvzs7O9ng8nTp12rx5s/aOhYWF9fX1ycnJCQkJxPl+kDD0DR1ay5IcHR1ts9lqamrKy8sbl9Q9cuTIV199NWfOnJSUFO3BxsGI5jZcp9O5XK4uXbpkZWWdPXt28ODBYc/5OXdCUbUqkleBFse5hB6Ter0emuZ++OGHx48fJ0myZ8+eMAUDogwURYVGGaC1M/zd5huCEEIEBhoQQqjN1dTU2Gw2k8lkNpt9Pt+VvoyDdACt59m1BZJ1RVFUFEVP/zp7MbjdbqfTCf07L6cLCWqZ0Whcu3bte++916NHj3/84x91dXWKorhcLigCQpwfyrb8NdFy+OGfoiiWlJQcO3aspKRk586dDQ0NPM8bjUaYoORwOMaOHXult6uVtm7d6vf7TSZTRkaG0WgM61XZmNlsLisrg1OHTqeDLrllZWUrVqwgCGLTpk27d+9+66233nzzzSlTpjgcDlIm4YZ5Xl7e6dOnu3c7V2SBoqipU6fefPPNDQ0Nx48f37Nnj06nmzVrVkJCgiiKycnJ2jsqinLw4MHIyMiIiAhYFEmSULYw7EMRJdFut0NGQ01NTeNvzcaNGyMjI6dNmxZWBqI1oEAvZDTATuB5PmwQ3u4uIRApCAKEFUwmU2ZmpqqqUNOHIqlAMGAwGKCRp/Z8iDLAtmMfHITQlYCBBoQQamNaDTYipBD6JUxzbf3z4RL52r1J3qEu8dscpHaHTpjXfnXtfmQdhKqqUOaQoqhvv/12xYoVcKN++fLl8ARZlkNvDof+3RyWZT0eT3V1tdvtFgSBJEmfzweHaEREREREBNwEFgThpptucjgcV34rL6ysrOyHH36AcePgQYMhX6AFiqJIkgQv0el0iqL07duXIIicnJySkpInnnjC6XSWl5d7vV69Xg8jc1mWCYKoqqo6efKkqqqDhwyG6hUEQYweNZogCFmRX3vttdOnT48cOXLx4sWN31SWZcjSD+3F02RrSaPR6HA4TCaTTqerrKyE50iSRFM0zdDZ2dk7d+7861//2rguBkmSLMtCIUlIKPN6vRazJTQrAb6JOp3OZDLJslxZWSmKYticgpZdWu7VlX5+WLAAqjbAz1AzuPEyjUYjpjMghK4cDDQghNDVgENKROBh0NZIkoTx1Y8//rhq1SpBEBiGOXHyROGJQniClqHQ+kCDw+GAcbiiKAzz/+zdeXwU9fk48Ln33myyyWZzJySEKxCQKwJyKBaLoIDiSa1V23q+qpVq/frzpMWztNp6UawKaBVvEQ/kiFwSjkBIAiGEhJB7c+y9c8/8/nhwuoZDjijYPu8XL19xMzvzmZlNXvk883yeh+E4juM4hmFUVYV6h7Isi6KoadrFF18sy/K58DQ4HA7DSMxmszfN+73bw0PsL7/8kmVZRVFycnIKCgoCgUB5efl555133nnnybK8du1aRVGSk5MTExNhqq8qaltbW3V1tdlszs/PT0lJiW8W4/P5ampqHA5Hbm5u/LGMi6/rejgcpijKmNye4EY4HA6z2QzXv62tDbpjSJLU3tL+5ptvXn311cXFxSRJ9lohoioqwzCQOhEKhUaMGGGxWE7QOpdlWaj3+d8d60QIobMCAw0IIYTQjwdjDX1LluVQKLRx40aWZT0ejyAIHo8H+ggQpxVooCiKYRiapmmaDoVCVqtVkZVAMABPwhmaUVTFYrGkpaUNHjz45Lsq/qBgTYfJZEpKSoKGDide4S8IQltb2759+xwOhyRJQ4cOTUpK8vl8Ho9nypQpqqJWVlYeOHBAVdXJkyebOBPM5yORSF1dXV1d3YABAwYOHNhrn52dneXl5VardejQoUcfUdd1VVX9fj/DMN/bQ1HTNKjRAAU4Dx482K9fP4IgeJ5fu3ZtW1vb1KlTofxkrwBBZ1en1+ttaGh46KGHbDbbY489NnDgQFVRj1dnAZYP8DyfmJjYa7S6rmuapukaTdLxr5945AghhAwYaEAIIYR+JBhl6FuhUMjpdDocjhkzZlxwwQUJCQltbW12u924zqcaaIC1+rAiA/IaIO0cSrrCNiRJqqo6YMAASLn/wU/yJITDYUmSnE6nw+6ANQ6aqp2gHiRFUWvXruU4rqenx+VyXXvttRRJud3um2++mSAIiqagc2cwGLz44ot5gYcgiyiKbW1tkiSlpaUlJyfzPM+xHEEQJEXqmh6JRERRTEtLS0lJIQjCKBIRfy/C4TBN0xaz5cSno6oq1GggSZJhmPb2dl3XeZ6vqalZtmzZHXfc4fV6j1n60el0wjqaIUOGFBcX22y2QCBgtVqPF2hgGAZu9MldZoQQQqcAAw0IIYQQ+klyOBy6rrMsO3jwYJh2Dho0iDh+IYz/1ifS0EO3u7t75MiR8PD/mG1uNE2LRqMMw5hMpjVr1gQCgaSkpOnTp2dkZBDfrtgXBCESiTQ2Nra3t48YMaK4uFgURYIgYrFYKBSqqqriOC4nJ8dsNlssFp7noVCCoirr1q3Tdd1utxcWFjI0I2kSx3FG60RN1SBqQJIky51onQLskyRJt9stiqKu683NzSzLUhT10ksveb3eWbNmQY+MXt12ZFk2m80dHR05OTl/+MMfrFYrRBA4jjvefZdlORAIMAwTDoeNejoIIYT6xI/XswchhBBCCPU5j8fT09Njt9tpmoYuGxRF9Zpda5omSZLD4eBY7tVXX+3o6HA6ne4k9/XXX+90OiVZgs1Yhm1paeno6KAoqqSkhOM4p9MZDoetVivP83V1dTRN9+/fH0qcWiwWaAVqMpl27doly3JhYWFmZibN0BzLkSRplGPQdI1lWbvdDt0QTnAuNE1DbMjlcjkcDlEUW1tbY7HY8uXLW1tb//jHP/r9ftiSYRjjHKEPCEVRUIvX7XazLAutFojj57BIkgRb/iQ6BCOE0E8L/mJFCCGEEPoJy8jISEtLUxSlra3N5/NlZWURBNGrUKWqqrDQo2xb2apVq0KhEM/zf/vb3yCdwehQQDN0Q0NDU1NTLBabMGFC/BS9vr6+sbExPz/f6/Wqitrd060oSnp6uqqojU2NTU1Nbrc7Ly8PunLQDL13715ZlouLiyEcwHFcUlKSoijhcPgE5wJFHzmOg5ABx3GxWMzv969YsWLmzJmFhYXHXK6iKIosy83NzZs3bw4EAlOnTk1LS0tISGBZ9gQ1GlRV9Xq9UOzzFC85Qgih74EZDQghhBD67wHP2E/sJItlHHOz01t/8YMWAnC5XKNGjeJ5fu/evX6/H0YIxRoMLMuyLBsOhz/88MMDBw7Isvx///d/Q4cOhc4RsizHYjHYsqurKxAIJCQkeDweXddDoZDD4VAVtba2VhAEk8lUUFCgqMpTTz31xhtvEARBM/T27dt5ns/JycnPz5dlGdpzLF269IsvvlAV1egH6XA4jCaXx2Nc3sTEROgqcuDAgccee8xms911112whsLYEjaGa1tTU/OXv/zFbrcHg8H58+eHw2H4rqzIehwi7l6oqup2u0+ptyVCCKGThIEGhBBCCJ1zYAkAQRAwU42fKBrIYyG+7bkAdQFURYXeEMFgMBqNGkUEj/neo/djbKmpWllZWSAQOHqzk6EoCkEQ27Ztq6iogG6U+nedybVyu92XX345RVGRSGTDhg0dHR2iKFosll5TelmWly5d+s4773Ac9+CDD1511VWqqkLNSIqkrFarMTl3uVxOpxN6MdisNoIgQuHQvn37EhMTi4qKUlJSWJbdsWOH1+uFSXtVVVVSUhJBEP369XM4HIqi7Nixo7q6evbs2XBoiqRomi4uLpYkqaen5wRJDYqi0BRNEERKSorZbKZpOhaLVVZWPvbYY8d7C0mSLMs+88wzDz/88CWXXHL++eeHQqGKigr4bq8QTywWUxRF1/W2trZoNJqZmWlELhBCCPUh/N2KEEIIoXMOSZIwT1Y19Xs3jkfRFCQ18AIvimJnVydBEIFAgOd5eMh/khNLXdchOqAqqiRJDz380O7du+Ex+6meC/FtcoTb7X7hhReguiGEPOKrDJx2uEFV1AsuuODGG29MTEz89NNPt23bRhCEkaEALRsOHz68YMGCJUuW5OXlLVy48NJLL+U4zqihAOEGq8VKkmRaWposy4cOHaqrq1MURVZkgiDefPPNtWvXMgzDcRxJkps2bbJarRMnTqQoShAElmWbmprMZnNmZib0znz++ednz56dm5sLZSlJiqRpurCwkOf57u5unue/96RcLpfVaoUMiKlTpxYWFsLrvQIHsiyTJPn2v98eNGiQN9VLEERVVZWu60lJSbAggqIoXdONf/AuWGOSlpbmcrnOkdYhCCH0XwbXpCGEEELonCPLcnl5eX5+PrRLPHkkSdIMLQjCI488EggEBEHo7u622+1mszkrK2v+/PlutxuqAJx4PxRFQUiCZujXX31dEISZM2fyPG+UMzglkJyfkZExd+7cp5566rHHHrPb7ZAycOYgTHDHHXe0tLSUl5fff//99fX1JSUlqampXq+3tbV106ZN77333qFDh0aPHv2b3/ympKTkePtRFbW4uPiiiy5auXLl4sWLb7/9dkVR/vnPf/p8vjlz5qxdu7ajo6Oqquqjjz666qqr7HY79HqYNWvWG2+8IctyRUUFx3FPPfVUUVHRjBkzeu3f4/Hk5eU1NDT4fD4owXDMCyWKIs3Qqamp2dnZXV1dNE3ffffd8N2jV6DAhR09ZvScK+bQDB2NRnfs2JGampqeng4VMTmOi19NA80vGhoaDh06VFBQ4PV6cd0EQgj9EDDQgBBCCKFzzl/+8pevvvrqt7/97S233HIaj/qj0eivf/3r1tbWN954o6Gh4e67777gggucTqfT6YRqhSezE1mWWZb98ssvV69effPNN2dkZBxzJDD7PZlECbPZfOGUC9euXbt48eLHH3ucIAhd00n6dFIkelE11Ww2L1q06OWXX66url61atV7772XmZkZjUZlWWYYpn///vfee+/kyZN5nhcEgaZpiqQgQgErTQiCUBW1s6vT6/XecsstiqJ0dnb+/ve/Z1m2X79+r732Gsuyy5YtW7NmzYMPPjhhwoQ5c+YkJCRAOsPgwYMfffTRL7/88s9//rOu69dde90VV14BE/j40IAoiiUlJZ9//rnP5/veM3I4HA6Hg6bpxx57LCkxiThOnQtVUWmGzszM5DiO5/lAINDS0jJ69Gi32w2nRlFUfFIMSZIURUFGw9ixY2HFh6ZpuIACIYT6FgYaEEIIIXSugHljOBzOzMy87LLLhg0bRhAESZKSJImi6HA4dF2PRCLGSnvjjdA9Aei67na7XQkumqbr6uq8Xu/YsWOLior8fj/LsjBfjUQidrsdtpckiaZomHXH03W9u7u7rKwsNzd33LhxxLcrICRJCofDbrdbFEWTyQQrLKA/AiwuOMEJ0gx9yy23XHPNNXPnzi0uLlY0hWboM5zownN7+PrWW28VBKGmpqa6ulpRFIvF4vV609LS+uX1g8vLsf8ZHlyK/9StoEiPx6Mqalpa2mOPPWas5oAsAIIgfvGLX/ziF7+IP7TJZILNrr7q6rlz5x45R5qOr4VhbJyRkTF27Nh33nln8+bNF1544fHO2kgxmDZt2gUXXDBt2jQirl5G73MnjrS00HWdY7nm5ubOzs7BgwfbbDbj4pAUaSQ10DQtimJtba0sy5MnT05ISDjVq40QQuhkYKABIYQQQucEWZY1TaMZ2maz3Xrrrbqmw+QfOjVKkhQIBCoqKhobG3t6emiaNpvNfr/f6XTm5+fHBxqAoiqNjY2xWGz06NGw/sJkMhEEQZJkLBaz2+1GfAGmqZC/EL8HVVWbmpoOHjw4depUIypBEATHcfDA3NghRVIEQVit1vi3G5kCvRQUFIwePXrp0qWLFi2CDc7wcXqvo5jN5uHDhxt1GY2TggYT/3kXRfYa55H/0iT1w9Tw0jW9sLBw8uTJhw4dam9vh9UTsVis13UzrsakiZPgA6Bp2vFKb1I0ZcSbeIGvqqryeDzZ2dkQGTlSCCNu6QTLsrW1tVu3bh0wYMCAAQPg4mA6A0II9Tn8xYoQQgihcwLLsiaTCR50UxRFMzTP85qmwWywp6fntdde27FjB0EQgwcPHjp06NChQ4d9K34/JEmKoijLck1Njc/nGzhwYGpqqqqoVqsVqjNYrVZRFFmWJSkyFosJggCdC3qNx2KxVFZWBgKBESNGHD1aaGYBwztmNkT818b/RiIRnueLi4v37dvX2dkJ8+G+ous6lK7keZ5hGCP/Qtd1TdNIivzOv1PvnXE8R8IlJAX/jreZoiqFhYWXTLvkwIEDX3/9Ncz/e7XhjBcKh6BsJEVRJzPIaDT69ddf5+TkDBkyBAo0HL2NIAiVlZU9PT3Tpk0bOHDg8TZDCCF0hjCjASGEEELnitbW1lWrVjU3N/fv33/mzJlQeVHX9Y6OjhdeeGHgwIGTJk3q168f8e382XgUr6kaTJ5hP5Ikqara2NiYmJjocrkgIYLUSI7jRFF8++23a2pqZsyYMWLECJ7nP/roo4aGBq/XO3ny5KKiIuLbRfvhcLiiosLlcsER43V0dHzyySetra3XXXdd//79CYLgeX758uX5+fnFxcWQ79ALTGhVVeU4bsKECf/4xz927tx54YUXfm9ZypOn67qma7qum0wmiqJURTXCDefCdFqWZbvdPnLUyJycnHXr1pWUlKSkpDgcjuNt73K5jBt69PiPThiJxWJVVVWXX355enr68d4VCAQ++eSTnJycq666CkJLZx5nQQghdDTMaEAIIYTQWRMKhYhvQwmhUOhPf/pTWlpacXHxSy+99PHHH6uqSlGUKIr/+Mc/cnNz586dm5Odoyqqqqg0RUNtAo7joBFm/IzRZDLJsrxmzRqPx1NcXCyKIk3RuqarivrWW2/xPJ+Tk/OnP/3p1VdfffLJJ51O55VXXrlv377HH3+8qamJIAgouyAIwoEDB7xeb6+8g+7u7scff9zlcvl8vieeeCIcDquKumLFildfffX999+HR/SRSATGI8tyfEaD1WplGKaoqMjpdO7atYtl2V4rGk6Aoqiurq5IJALJFEcjSZKmaYgyEARBM7RxTcij9HrjaTDeSMQtxCCOP3VnGEaSpIyMjNtuu62pqWndunUnPt/4/cQfF0IMJElGIhFFUeDrYDBYWVlpsVjy8vKI79Z0gHwTkiKjseh7773X0NAwb948u91+vBUToVBIFEWCIGKx2LkQoEEIoZ8iDDQghBBC6KxxOp3wRWpq6v/7f//v6quvnjFjxs9+9rO2traamhrIaCgrK2NZ9rLLLoNFCsY/o3mEqqow0YWJpSAIHMe1t7cHg8H09HSXywXPrmHC+cUXX1x33XXp6emHDh2qqKi46aab5s6dO3z48PHjx7e3t1dUVEiSBPN/VVV5nofQQPyY//73v8+YMWPq1Klms7mnp0cQBJqhJ06cOGzYsEAgAGN2OBzBYFDTNGNpQHy4AXR3d5MkGT9FPzGe59PS0mxWW6+iBgaSJI83eT4XntubTCaSJBmGGT58+GWXXbZ8+fLDhw+Hw+FT3Q+sdyBJ0uFwwJ0VRdFhd+zYscNmsw0YMODoQEw0FqUoqqKi4tNPP73xxht//vOfn2DJhtVqDYVCLpcLS0UihNBpw0ADQgghhM4amHvzPF9WVuZwOEpKSgiC2LJlS/w2wWAQyj2aTCZIZzCyAFRFhUlm/ESaoihN07Zu3aqqakFBQVpaGkVRsiLzPL9x08axY8fa7fadO3empaWNHz8+JycHEiJ4nofn2AZJkoLBoNPp7DV737Vr189//nOapj/99NOMjAyLxSJJUl5eXkFBgdfrhUYMBEF88803DzzwgMlk0rXv1CM0tLe3q4oKaxxO5lrJstzd3c2ZuNNbbXHi3ITTZuQOUDQF1R9OsDEs5WAY5tZbb50xY8bvf//7YDB4eofTdT0Wi91+++2TJk2SJIkX+NLS0pycnKlTp0JCCiBJUpZlp9PZ2dn5pz/9ady4cTfeeKOu60eX5DDIsuxyuaB4xymNDSGEkAFrNCCEEELo7NA0DdoiWK3WsWPHjh07Fl6vrKxMS0sbOHAgz/MWiyUQCGzdunXDhg1utzt+7gcz/KSkpJycnGuvvdaYMHMc5/P5tm/f7nQ609PT4fm/oih2u33kyJFDhw5VVbWqsiohIWHGjBmKokBWxe7duzVNKy4u5jgOjqvrejQaNRolGl5//XVVUdesWZOYmFhSUmK325ubm5OSkhiGGTFiBEmSkAfR3NwcCoWCwaDNZqNIyog1GIsOuru7FVU5upDkMem6zjDMkCFDbrjhhviJ9E8OwzAw/pt/jUqXAAAgAElEQVRuuolhmJqamszMzJN/O0mS4XDY4XCIorhly5b9+/cHg0Gz2XzvvfdSFHXbbbdJkgT3zngLxBTKy8uvv/76a665RpZlk8kEt/iYh8jJybnyyiuTk5Np+qRuDUIIoaNhoAEhhBBCZwf5ba8HmBbKskxTtKIqO3bsSExMHDFihMVi0TRNFMUBAwacd955LMsayyUIgjCZTNFo1Gq1pqenk9+WPIQn2AkJCTt37nS73UOHDoWN7Xa7KIoOh0NV1PaO9qrqqgkTJiQlJsE8f9euXe3t7QUFBRB0sFgsR9pSkKSmab2GbbFYaIZevHgxDJIgiIyMDJIkBUEYPHgwwzBwOr/4xS9+deOvZOU7afzGg3Sapp1OJ+RonGSswWw25+XlQQ2CcwcZV2zyJJMjGIbRNM3hcNxxxx3Ed0s2fu8edF232WyqopIkOWXylM8++6ypqem6667Lyspav369sU2vtxAE8bOf/Sz+lfgPUi/Z2dnZ2dlHv34uLD9BCKGfCgw0IIQQQujsgAR4439VVaUpGrpFZGRkFBUVaZomCMKAAQOqq6uHDh3qcrl67UGW5fhQBXzBsmxVVZUkSWazOf5pOayAUDV1z549Fotl8ODBFH1kTURjY+Phw4dvvvnmhISE1tbW9PR06FuZlJQUCAR6HdRsNgeDwUAgUFRUlJaWBifS3t7OMIzFYoHChCzLtre3Dxw4UFGVozs+whoNaE6h6RpNfH+gAU7QaLVwShP7c9CZj5yhGYIgFi1a1N3dTRCE2+3Gwo0IIXTuwBoNCCGEEDonMAxDM/SePXv8fn9RUdGRlgE0PXbs2J07d+7evRtaVMQ73kr70tJSmqb79++flZVlVECAmo6apq1bt85ms40fP54kSVVRY7FYeXk5TdPjxo2TZfmRRx7p7u6WJImiKK/X297efvT+ofVDYmJiUlKSruvBYHDjxo15eXlpaWk8z+/atevFF1+cP3/+gQMHTCYTVC6Ir1/g9/sVRcnIyJBl+STz83+6MYU+BxU04Z+u6w6Hw+FwGK0ozrzqBEIIoTOHgQaEEEIInTXxc0IIBGzatMlsNo8aNUoURYqiTCaTyWT65S9/uWrVqrKyMvlb0MSB53ljP5p6ZI1DV1fX9u3bzWbzsGHDZFnWdI0gCKMTga7re/bssVqtQ4YMIQiCF/hAIFBTU5Ofn5+SkrJx48aUlBSWZRMSEiiKSkpK6unp6TXmcDjMcRxMbnVd11Stq6urqqpq8ODBLpcrFArt2LHjnnvuUVW1s7NTkqT498IwwuGwpmlutxv6d/4QF/Z/ga7psNyGpmhFUY5e5IIQQuhswaUTCCGEEDprjLUAgiDAs/3Vq1cnJiZeeuml8ZsNGzYsFArt2rWL5/ns7Ozc3Fyz2Ww2m6GeHyygUDV146aNu3fvjkajPp+PJMmysrJIJDJmzJgBAwbQNJ2QkADrGg4fPjxx4kRJkmRZttvtsiyfd955hw8fXr16dXNz81VXXeVwOAiCMJvNJSUlq1atqqury8nJiUajFEU5nU6IMkyePHnXrl3Lly/Pzs7euXPn7NmzIXIhiuJtt922cuXKcDg8bNgwmvpOwgJFUrqul5eXMwwDrStO/lrFB2XOtYf2P9p4jOU2JEmSNGmUt+i1/ORcuz4IIfS/BgMNCCGEEDprNFWDuaLZbBYEQRCESCTSqxSfrusJCQmXXnppRkZGeXm5z+fbvHlzIBBgGEYURV3X8/PzZ82aZbVa+/fvHw6Ho5Ho6NGjGYbp6uqSZTklJSU5ORn2oypqQkLCo48+OnToUCiRQBCEy+WaMmVKQ30Dy7Hz5s3LysoiCKKrqys5Oblfv36BQKClpSU/P99sNkO7BJPJRBDE7bffvmfPnn379rW3tU+ePHnYsGEQNMlIz1A1dc2aNZMmTbLb7YIgxHejpBlaluWtW7fm5ORkZWVhOsOZwGgCQgidszDQgBBCCKGzBsoWHDhwwGazeb3eVatW6bo+ceJEY4P4Cn/Dhw8fMGCA3+/v7u6ORqOqqkJVxby8PFhPkZ6enpaWRlGUpmkwhxdFEeICqqJqusayrJk2X3311dDMIhKJOJ1OKANhNNcE0J+isLCwf//+u3fvHjt2LIRCYM+6rns8nimTp0ydOtXohQmBBpqhDx081NjYeN99923bts3tdvfr18/YLc/zXV1dBw4cmDNnzilFGXoVzvxfZvQHPdsDQQghdFwYR0cIIYTQ2fTSSy/dc889u3btoijqgw8+sFgsU6ZMMb4Ltf1UReV5PhQKWSyW9PT0oUOHlpSUjB8//sILL7zgggtSU1NtNhs0tjRm7xB6iC+RABN1SZJ0XRcEgaIot9sNX8iybNSMlCQpHA5LkkQzdGpq6oUXXrhx48bOzk6CICiKgoaXJElGIhFJliKRiMVikSSJJEnYTzAYLCsroyiqoKBg3bp1+fn58VNiQRA++eQTiqLmzZv3I1xbhBBC6KzAQANCCCGEzhpd0/fs2dPY2OhyuR599NHm5uY777xz0KBB39lG1wmCMJlMdrv96Kf60FSSZVmaohmG0TQNigLSNE3TNLxF13WKpqBcAsuyuq6bzWaaplVFtVqsmqbRNE3RFOwcSjA4HA4IUowePXrYsGErV66ERRAkSeqaThCE1Wo1mUxWq1XTNDguQRAUSZnN5vHjxw8YMGDZsmVz586FQYqiCBETi8Xy+eef33nnnTzP63FO5lphVwUDXgGEEDrHYRoeQgghhM6mFStWLF26VBTFIUOGzJs3b9SoUcbCB4IgjkzENR0WWRw9zY7/S+bov2qOnpF+Z3tNJ75dvnF0Qr4sywzDKIqyf//+F1988ZZbbhk+fPgJTgTGpus6NMUgSRKqM0iSxHFcOBx2OBwPPPCAx+O58cYbExMT40eCM2eEEEL/TTDQgBBCCKGzySijQBCEqqhGHwEAf6gYzSmIo+bkZ/KXjNG/wHjF+Nr4FjTR/Prrr9euXXvzzTcPGjQIlk4cb5/G2gpYysGybCgUcjqdmqYtW7astrb2oYceikQiRinKY54UQggh9JOGgQaEEEIInU2CIJjNZgg3hEIhXdctFovRqeFsBRqIuKiHJEksy8K3YInEieMCJElChUg4O4qiOI6DnhoOu8OIpGBGA0IIof9WGGhACCGE0Dnt5Cfkp7p04sRbxq/gOJk9nMwIEUIIof8FWAwSIYQQQugYjtd+8sTrJn6w4SCEEEI/GZjRgBBCCCGEEEIIoT6DGQ0IIYQQQgghhBDqMxhoQAghhBBCCCGEUJ/BQANCCCGEEEIIIYT6DAYaEEIIIYQQQggh1Gcw0IAQQgghhBBCCKE+g4EGhBBCCCGEEEII9RkMNCCEEEIIIYQQQqjPYKABIYQQQgghhBBCfQYDDQghhBBCCCGEEOozGGhACCGEEEIIIYRQn8FAA0IIIYQQQgghhPoMBhoQQgghhBBCCCHUZzDQgBBCCCGEEEIIoT6DgQaEEEIIIYQQQgj1GQw0IIQQQgghhBBCqM9goAEhhBBCCCGEEEJ9BgMNCCGEEEIIIYQQ6jMYaEAIIYQQQgghhFCfwUADQgghhBBCCCGE+gwGGhBCCCGEEEIIIdRnMNCAEEIIIYQQQgihPoOBBoQQQgghhBBCCPUZDDQghBBCCCGEEEKoz2CgASGEEEIIIYQQQn0GAw0IIYQQQgghhBDqMxhoQAghhBBCCCGEUJ/BQANCCCGEEEIIIYT6DAYaEEIIIYQQQggh1Gcw0IAQQgghhBBCCKE+g4EGhBBCCCGEEEII9RkMNCCEEEIIIYQQQqjPYKABIYQQQgghhBBCfQYDDQghhBBCCCGEEOozGGhACCGEEEIIIYRQn8FAA0IIIYQQQgghhPoMBhoQQgghhBBCCCHUZzDQ8F9Cj3O2x4L6zLl/N8/9ESKEEEIIIYR+ZBho+AnTNK3XFwCDDt8rGAwSBCFJ0jl+iUiSlCSJ53lBEOKHek7dX0mSRFE8RwaDEEIIIYQQOutInB781zDCDSRJGi/Gf416iUQiHMexLPuTu0rGj+05MnJN0xRF4TjubA8EIYQQQgghdPZhRsNPVfwDbV3XeyU1oO+lKqrNZtN1XZblczncpiqqKIoEQQSDwUgkQpx7qxUkSaIoCqMMCCGEEEIIIcCc7QF8P13Xz/yxraZpFPWdqIosyyzLnq3xnMaxjMiC8UpPT09paSlBEJMnT05MTPxxhnROOfq2nqRYLLZ+/fpwKDxx0kS3262pGkVTp3RbNU0jSfKH/iQIguDz+fx+f21tbTgc9ng8w4YNy8rKUhSFZdlzJOKwf//+hoYGj8dTUlJytseCEEIIIYQQOvvO6UADTCNJkgyHww6HQ5KkUCgUCoUURfH7/QRBJCYmKoricDg8Hg9DMzRD93p7bW0tRVEmkyk9Pb2zs5OmaUEQMjMzdV1XVZVh/nP68TNGVVEbDjVwHCfLcn5+PkEQpaWldXV1V155JcMwFrOFZuhAICCKosViMZvNP0TuvSRJ8IiY53mO5Sia0lRN1VSaokn6yLF8Pt+LL74YCoWsVuu0adOOPpH/PqqitrS2fPzxx7m5uTNnzlRV9TQCDYIgBAKBp59+uqenJyMzIz09HfZM0f/Z1Qkuo6qoBEHAvTDe8gNd9u7u7gULFuzZsyccDttstkAgYLfbly1bVlhY+MMd9FR98sknH374YW5u7nvvvXe2x3IO2bJly/bt2y+99NKCgoKzPRaEEEIIIYR+VOd0oIGiKHik73A4eJ7ftm3bRx999M033/T09EAyOUQQRo0alZWVdeutt/YKNIRCoQ8//PCDDz649tpr77zzzmeeeWbXrl3Dhg3785//bDabaZo+zmGJ1V+tfvDBB91u9+9+97v8/PzKyspHHnnE5/OlpqbOnDlT07RQKPTQQw8dPHjwuuuuu/7663+Ic+c4DuIsFosFXiEpkiZo4tzLnP8xyYq8YsWKN998MzExcdq0acZV+jHHQNGUJEkMzcQHJn4g8+fPr6ioKCwsvOqqq4xAQ25uLpx4LBaz2+0/9BhO3o9/L85ZPM8//fTTe/bsgd8VZ3s4CCGEEEII/ajO3UADhBhIktR1nef5119/feXKlY2NjdnZ2UVFRSaTiSAIURRbWlpKS0slSbrsssvy8vLi97BmzZo1a9YkJydfc801HMeNGTNm/fr1Bw4cKCsrKykpgQn8MSftZWVlPM+np6f369ePIAiXy+X1euGIhosuuqi0tHTZsmXnnXfegMIBvWIcfQIe1zc3N5eXl48YMSIrK0snv6fRwDnyiPuHQ9O01Wp1uVwej4emaIIgfuSZbXNz89atW0eOHJmXl/dDR3zeeecdn89ntVr/7//+r7i42GQyQWoPfBdWT/ygA0CnzWKxZGdnBwKBpKSksz0WhBBCCCGEfmznbqDBWDGhKMqWLVtee+01giBmzpx5xRVXuFwul8tFEEQgEGhra6urqwuFQnl5efFlF/bu3fv++++HQqH7778/OTmZIIgLL7zwhRdeCIVCq1evnjJlygkOvW3bNpfLVVJSMnjwYFEUvV7vvffe6/P5JkyYEIlErFar0+n82c9+Vltb+/rrry9duvTBBx+02+19PsmHc1m3bt2iRYsef/zxrKwsTdVIioSLo6kaSZIURcHqkr499I/jVKtdqIrKsuyvf/3r/Px8WO8AL/4QUR5gLGAxVFVVPfnkk88880yvqNYPYc+ePT09PdnZ2WPGjJEkiSCI+DQchmFOI8jS65r3ykGAwhPEKUasVFUlCEKW5dMexg/nx6yoEk8UxXvvvXfbtm1Dhgz58Y+OEEIIIYTQ2fWDPA2GJ72xWIzn+TPZj8PhUBWVYZgPPvjAYrH069fvgQceGDx4cF5eXkpKisfjKSwsnDRp0k033fS73/1O0zRYTwED2LJlS1lZWUZGxoQJE+D15OTkcePGSZK0b98+n893zCPGYrGVK1fu2bPHZDKdf/75uq5DjvqYMWOmT5/ucrk4jqMoqru722KxTJo0yev1fvXVV7t371YURZblvm39AHtraGjo6elxu90EQZAUqShKOBwmSVLVVAgxAIqidO1IDoiiKBCGOMEUi+f5WCwGX4dCIfhCFMVwOKwqKpQhOAHYoNfcEnYYi8VkWZYkKRwOH/1GURThcHAWxmAEQTAOCicOU+t4iqoQBMEwzLRp04YOHRqJRjRNEyWxVwMOaM0Au4V9at8y9q9pmqqq8feLZmgYEkmSoijCGxVFMfagKmokEqmoqOjp6enp6SEIgiTJaDR64uvc2dlJxHUelSQpFosJgiDLcq/zPfrEeZ4XRRHKi3AcFwqFVFWF7+q6bgQIOjs7NU0LBALGufe6L8aBjpeCoWkavIWiKPgi/nLFX95gMHj0/nVdd7vd8T+A8AEAxzyiruswKkEQjDEYvy5URZUkCY519FB7nYvxCnyu9DiqomqqZmypKqogCIIgGK/EYjGo9mLsM/7EjXMxPlHf+3NhMJlMWVlZc+fOHTx48Em+BSGEEEIIof8aZ5TRAE8LVUVtbWvVdb25uZkgiMzMzOzsbJhgGPUFTg88T9Z1fd++fQzDZGZmQiJD/ABgzqBrOhH3vLe5uXnnzp0kSV5++eVut9tIc7j66qtLS0t7enq++eab6dOnx2eew36sVuu6detcLld+fv7AgQNJkhQEwWw2+3w+s9ksSVJCQgJBEFD9sbi4uKSkZNmyZZs3bx49erSmaX2Yyi4IAsdx5eXl1dXVNputtbW1q6sLnj/DDIokSU3T4udU8MYDBw5EIpHU1FS3201R1PGGZLFYjAlVU1OTJElQU9NmtdEMHYlETrz4v76h3uVyOZ3O+BetVivP87Isx2KxaDTq8XhisRjDMPFJATBta2lpGTRoUPxg4Ivm5uaGhga3222xWPLy8no9b4f5fCAQoCiK53mPx0NRlNVqjcViZrOZJEmYALe3t4fDYV3XWZbNzs622+zhSFhVVUEQ7HZ7QkIChBgAFPXkOE4QhI6OjqamJrvdnpqaClkSDMPE+JjVYqUZmqCIXbt2HTp0COIpra2tiqIwDNPd3Z2Tk3O8C5WcnKxpWiQScTqdoihqmtbV1dXS0mK1WjMyMiDXhqIo+HnhOE4URZPJpOs6RJScTmdiYiJMjxmGiUQiNE1zHBcf2khKTBJFEVYYwV3r9cGWZCkaiBIEYbPZzGZz/PAoivL5fNFoNDMzEw6tqirLsoqiQPQqEAhAVM7j8bhcroSEBLiw8TsxmUyRSCQtLc24j8FgsKWlhWVZj8fjdDo1TYs/LkRJQpFQe3t7//79CYKAQzc2NsqybLPZvF6v1Wo1No5vvKKqKvywK6rC0Ezj4cZoNOp0OhmGgXOHxVZHzo6mZFmmGVrX9Y6ODr/fb5xLSkoKhFc0TQuHwy6Xy+FwaKqm6RrUiBUEQVGUUCjU3d0dDoczMzMzMzNPPndGlmWIRiUlJeEKF4QQQggh9L/mTJdOCIKwfv36d955RxCEpqYmgiCysrJGjx59/fXXw0P4viKKYiAQMPLkdV3X1N7pAxaLBaYl+/btq66uzs7OLikpgb/yYSZTXFycn5+/c+fOTZs2TZs2rdcEQFGUzs7OjRs3chxXUlICUyOIMvzxj3/0+XwPP/zwyPNGwouRSMRithQVFQ0ZMmTlypU333xzSkoKTFn7JFU7EoksWbKkrKysvb09EoksXrz4vffeEwSBZVld09//4H2aoI0HsLqud3V3LV++vLy8PBKJBAKBaDRaUFAwceLEyy67LDMz8+j9S5IUjUafeuqp+vp6+JqmaZfLddddd40aNarXeoFeurq6Hn74YZ7nZ8+e/ctf/rLXLVi5cuW///1vXdevuuqq6667rtd7P/74448++shisTz//PNG2AgqfX766acVFRWQM8JxnMvluvrqq0ePHm1sxnEcz/MffPDBN99843Q6Fy5cqOu6xWKBSanP56upqVm8eHEwGOzq6qIoCubVNE2rqmq1Wjs6Oi688ML58+eb4qSmpppMpr/85S979uzheT4cDjMMk5iYOG7cuOuvv97hcMCliMViTz75ZHl5eWtra2Zm5iuvvLJkyZKkpKRoNGoymZYtW3a8Kwb5DhCR+fLLL1evXr1v3z673a6qaiQSGTRo0MUXXzxnzhxIWIBQSEdHx6effrp58+by8nKTybR+/fqqqipFUSiKKioqeuKJJ+L3ryqqrMgWi+U3v/lNIBCYM2fODb+4IX4+TJLkW2+9tXLlSoIgZs6cefPNN8e/XRTFt95665tvvsnIyFi0aBHEnkiSZBhmw4YNK1euPHjwoFF41WKxzJ8/f+TIkUefJvx8hUKhbdu2ffXVV/v27YPSlQzDTJo0ae7cuR6P5z9lTUmS5/nPP//8X//615133jljxow1a9YsWbJEVVXj+o8fP/6uu+4ivruIgyRJlmWDweCGDRtWr17d1tYWi8UgMuJyuZKSkubPn5+dnR0/MI7jJElavXr122+/HYlE4FzgB58kSWhVE4vFrrnmmlmzZtntdlmQWZYNh8OHDh165plnBEGIRCKCICQnJ2dmZs6fP9/pdJ5MAU6WZZ977rl9+/Zddtllv/rVr753e4QQQgghhP6bnM7SCeOBoSAIzz///FNPPXX48OG6ujpRFEVRrKure//992+77bba2tpTWrZ9NFi2QJLkoEGDAoFAZ2dnKByCxP5eUQZN14wABEEQzc3NVVVVo0aNysjIgA2glKOmauPGjbNarZ2dnZB/AS/CbJCm6R07dnR2dubk5BQXF4dCIZ7nA4GALMt1dXXt7e1tbW1wCIZhWJalGbpfv36pqamBQKC8vJwgiGAw2FcFAhVF+eKLLxobGymKSkxMlGUZViKEQiFJlo7kcXxL07S//vWv77//fl1dna7rVqtVUZSqqqoVK1Z89tlnxpnGKysr+9WvfrV+/fq6ujqSJBMSEkRRLC8vf+yxx5YsWQKJJMcbm91ut9lsPp9vzZo1RNznAZ4Pr169urm5ua6ubvPmzfH58MZx6+rq+vXrZ7Va4Yx4nl+3bt2jjz761VdfRaPRxMREi8XS0NBQXV29aNGi0tLSQCBgvF3X9WAwWFtbW1tbK0kSwzCQYN/R0fHRRx898sgjVVVVbrf7iiuumDdvXk5OTkdHR1VVVVdXl9/v79ev3+DBgxmGIUkSkhpIkuzp6bn55ps//vjjhoYGVVHtdnssFisrK3vmmWfuv/9+QRCCwaCmac3NzZs2bYpGoyzLxmIxmHxCjklXV9eJ4zIEQcRiscWLF//973//+uuv/X4/ZB+0tbWtXr362Weffe6554LBoNPpdDqdFEWVl5e/+uqrzc3NiYmJoijCqhxRFFmWlSSp133R9CPJArquHzp0aNu2bSRFGusRQGlpaWNjY2NjY2lpqXEZYT8mk6m9vf3gwYMsy8qyTFEUPM//7LPPXn755S+//LKpqclms9lstqampn379i1YsOCTTz6BeIQxkkgkwjCMoihr1qz529/+tm7duq6uLkghaWxsfO+99+67777y8nLjkwCJKtFotKqqyufzvfvuu6+88kpbWxvHcSkpKQRBVFZWfvTRRzfccMOWLVuOvpjvvvvus88+++WXX5aVlREE4fF4FEU5cODAjh07rrnmmtbW1l7bv/jii0888cT+/fuTk5MnTJgwYcIEj8fT1NTU0dHhD/jD4bDb7Xa73XDiZrM5EAi8+eabt912W2Vl5eHDhwVBMJlMLS0tlZWVv/3tb3fu3Bn/aTzeHdc0rbOzs6Ojw1ighBBCCCGE0P+OU85ogL+tZVmWZfmll17atGkTxBd6bVNXV7dw4cJXXnnlzNOGeZ6/6aabqqurW1paHnnkkQULFlgsFni4TRCExWIRBMFIsKcoqqmpqbq62u125+XlQdq28V2aoUeMGEFR1KFDhzZv3pyfn6/ruqqp0DZSFMV///vfGRkZDodjzJgxoihGIhGbzdbd3U1RVCgUUhRF0zVSJ4lvIxcDBgwQRZHn+S1btkyePNnj8aiK2ieFL7xeb2lpaWVl5cKFC7dt2/b8889Pnjw5vhKBUZ1BkqSFCxeazeY777xz1qxZVqtVluVAIPDiiy9+/PHHb731Fk3TxnNsmCJGIpEnnnjC7/f3799/wYIFGRkZcD2htuVzzz1XVFR0wQUXHC81w2w2T5s2befOnT6fr6GhITc317hTHR0dLS0tqqq63e69e/cGg8HExETjFtTX13d2dkaj0aKiIkVRHA5HJBKpqan585//HAgEZs2a9atf/SorK4uiqLa2tpdeeun9999/5ZVXMjMzR40aZTT7pGlakiRRFO12u67pkMxfUVHx+uuv8zw/f/78efPmQQWKW2+9tbS09I477nA4HI8//vjw4cMZmhEl0WKxqKqqKApN04sWLYrFYrNnz7733nth+Yk/4H/jjTfeeuutr7/++p133oGUjfx++WvXrj1w4MCCBQu2bt36xBNPTJo06eTv5vLly19++WVN0+bMmXP33Xe7XC6SJEOh0GuvvbZq1ap3331X07R77rkH1oBMmzZt2rRpBEHcfffda9asueSSSxYsWBBfBjJ+fkuRRz5tc+fOra6ujkQijY2NRqFKXdcPHz7c0tKSmJjY09PT3d3d2trq9Xp1TScpkiTJuro6WJUzatQoeAvLshUVFQ8//HAwGLzrrrvmzp2bkJAA6Qn333//9u3blyxZkpqaOnbsWFmWIcVA13Wapv1+/8KFC/Py8t5///2srCxRFFmG/eLLL5599tk9e/Y8/fTT77zzTldXl9Pp5DgOik24XK5XX33V7/dfccUVN954Y0FBAYxhxYoVTz75JAy7pKQkfvmMqqhz585dvnz5fffdd8MNNxivf/zxxy+++GIoFPrrX//6zDPPGK9/+umnq1ev5nl+wYIF06dPN17/17/+9eyzzyYnJ7/44ouQBGGsJFq7dh4f4ksAACAASURBVO2SJUt0XZ8zZ869995rtVoDgUAsFrv99tsDgcCzzz47fPhwWEJ14kqToVBIlmUo0oEQQgghhND/lNOsWs+yrN/v//rrrzs7O+Gh69FbVldXL1u27EwGBxMqiqKGDBkycOBAhmFKS0uhzkI4HJYkCWYgNE1Dp0MgCEJLS4vFYoHJQK/K/AUFBcOHD+/o6Kivr49EIkYlP1EUm5ubN2zYkJCQcMEFFxAEwbIsPGGOr7nYa4SwCt1qtba0tMBoNb0v60EeDyxEVxTFZDIJgiCK4sKFC+fMmWO1WkVRZBgmKTHpnnvumTJlSjgc7urqMqrZweTwpZdeOnjwIEVRixYtysvLgwfyiqLcdtttJSUlNpvtqaeeOvGT2DFjxpAkGQgEamtrjctiMplKS0tjsdiECRPS0tIikci6deuMYooEQdTX17e2trIsO2LECMgroWn6gw8+6OjomD59+sKFC7Ozs6HaQlJS0hNPPDFkyJD29va3335bFMUTdFgQBKG2tjYSiYwbNw6m6CRJwsqdkSNHTpkyRRTFL774QtOOpL1oqgZ1CmF9/gMPPHDHHXcQBCFKIi/wycnJv/71r0eOHElR1KZNm87wTkmS1NnZ+dZbbwmCMHfu3EcffRSiDARBOJ3O3/3ud9dff73f71++fPnGjRuNwgQnj6KPXBbobuDz+erq6uAVSNlYs2ZNLBYrKioqKiqKxWJ79uxRVRWiDARBlJeXNzY2OhyOCy64AGKCoVBoyZIloVDohhtuuPvuuzMyMqCjSmJi4uLFiz0ez8GDBz/++GPI64EDud1ujuO6u7sLCgpee+01KNFiNptphr7ooouefPLJ3Nzc2tra7du3JycnQzUKaFRBEIQsy3PmzLnvvvu8Xi/UsCAI4sorr1ywYIHT6ayurv7ggw+Mk1UVVVEVp9O5fv36efPmxVdtnDVr1qRJkyRJ2rBhgxH3lCRp27Zt1dXVl1122fDhw+Ov26xZs0aPHt3Y2Pjhhx8euZIUBYdYvHhxKBSaOXPmH/7wB7gjLpcr1ZP617/+1Z3krq2t/eijjwKBQK8CIr38RBvBIIQQQggh1CdOOdBgzBC2bNkCUQajQiHx7Yp0kiStVqskSUaq9mnQNE2SJOhYaTabn3vuudmzZ6empra3t997770XX3zxv//978bGxq6uLl3XaYY2lmnEYrHa2lqXy+XxeI5uA6Fp2vnnn8/zfEtLS1NTEyxH1zTNZDJ98cUXLpdL1/WLLroIToplWY7jrFYrFAKwWq0sy0LoQVVUeFdubq7T6aytrT1mtOUHxbIsTdO6ro8YMSIvLw9S6BmaIUmSZmiTyTRp0iS/39/c3Ax16QiCMJlMoiiuWLHCZrPdfvvtUGiwq6uLIAirxUoQxE033QS1/VasWHGCQ+fk5BQWFoqiuHXrVuNFk8m0cePGzs7OyZMnjxo1KhqNrl69mmZokiRjsZiqqBUVFT6fr6ioqF+/fgRBpKSk1NfXv/POO2lpadddd10wGBRFMRaLsSzrcDhIkpwzZ46u6/v376+trYVDQIVCwvikUSRBELIst7W1CYJQUFAAufeA53mn0zlkyJCenp59+/YdGSRnij8RmqYvvfRS2InVaoWlE06nMzMzMykpqb6+/shxv401neo9Ikly8eLFuq6npaXdeuut4XBYUZRIJGI0d7juuusKCws1TXvhhRdOvqlBPPiQFxQUZGZmhkKhvXv3wusURYmi+Nmqz4LB4MSJEydOnBgMBtevXw+fbTiX6urqnp6eYcOGeb1e+BE+cODAli1bCgoKLrnkEoIggsFgLBYzfo4efvjhaDS6v2Z/U1OTsc4CBIPBmTNnWswWnuchmgAJGmPHju3Xr58kSRB27FXgwO/3z5w502Kx2O12k8lEURQkKI0YMWLIkCGHDx82bgFBEDRDS5IENwJqefznWzQ9btw4m82maZrRriIYDPr9fkmSRowYAXU3DXa7PTc3V9f1aDQa//o/XvhHZ2en0+l88MEHoXcGQRCqomq65vV6hxUPS0tLe/vtty0Wy2n0FkUIIYQQQuh/xCn/rQwp3DzPl5eXQy5DfFK3AZ6OHq+L5MmAinHEt1PKUCj0xz/+ccGCBVOnTs3NzWUY5vnnn7/yyitfeOGFHTt2GE0TCYIIBAL19fU2mw1KG/TabVJS0tSpUzMyMqqqqlpbW4PBoNGBb8OGDQ6Ho7CwMCc7B16ERABFUaAbYq8l2bIsMwzj8XhsNpuR0fBjcjgc0JhjxowZiYmJ8CI84vb7/RzHQWWHnp4eo/MfQRBQYtBsNo8cObKzsxPaanR1dUF/x9zc3KSkJFVVT5wTThDEmDFjGIYxQgCqonZ0dITDYYqiBg4cePHFF9M03dbW5vf7oSaCoir19fWyLI8bNw6CMpIkHTp0SJblvLy81NRUiqJgDgytHwVBGDlyJMwbKysr4yfhvS41NLagaVoURbjjZrMZvlYVFcoBJCUlMQwDPQhohoa6AxzH3XTTTaqims1mqDJIEAQ8xC7sX0hR1IEDB3rFquK7J57MPQoGgzt27JBledSoUVCFAcJDkBOkazrLstOnT4/FYp2dneHIMRqCfi/jTo0cOTIUCvl8Ppghw4oSTddgrcqQIUOgPadxRsFgcO/evQzDXHTRRQRB8Dyv6zoseOnfv39SUlIoFIIGK7BUStM0t9tttVplRd61a5dxI7q7u+FXwfTp0/0BP8dyDofDSEJRFXXs2LE2mw3eAkenaZqmaYqiJk6c6HA44NcF/LhBvIxl2fPPPz8cDhsJGsDhcMAeenVUoSjK6XSazWZFUeD3hq7rELSCbItetUI4jvN4PMRRXVQrKytJkjzvvPNgaQ/k9URj0UAgYLFYUlJSBEGIRqNwQU7jZiGEEEIIIfS/4JRrNEDyAkVR9fX1UCaNYRgjnQG2gRl4SkqK8Wjx9CQkJMCMVNf1lJQUXddLSkpKSkq2bdu2fv362traw4cPQwXEa665ZurUqfAuQRA8Ho/VYjWbzVAoMf7ZI8uyDMOMHj36tdde27x58/jx46HawubNmysqKtxu9/Tp02mGhhUQdrs9fmJpPL+FJHxZkQmCcDqdkiSZTKZgMOhwOCiSOubK7YaGBr/fL4qi2WyG9Q4mkykxMTE9Pf20rw904KNp2mw2UyRlTMVphobKCFlZWQ6HQ1VVyEOBUTU1NXV3d7tcrm3btkGRP5iEUyRFUiS09PP7/fv37z/ecXVdF0Vx/Pjxr776qqIoe/fuHTx4MEVT69etDwaDSUlJubm5UKahu7u7rKzskksuMZlM+/bt6+zsTExMnDRpEkwROY47fPgwQRCxaMwo+wdNB1VVtdlssVjMarVC2oUkS2baDKfQa/IPTQcYhmltbY3FYlDY0mQyybIcCocqKyuTk5OdTidN07qmw92Bia6qqunp6dD+ECpiGvtkWIbneajmcLxn1/GxhhPUs2hubmYYJr7NKsdyBEFANw1d1/Pz8+FbTU1NRUVFxnuNNTtGIYajxR93woQJH3zwQVNT06FDh7KysiAZJBgMjh41euDAgaqipqam8jy/Zs2amTNnEgTR0NAAP86TJk0KBoMQ4qmvr4fOF6WlpTCZhywSmqLbO9ptNhtN0z6fTxAEmGmrqgpFW9PS0lwuF1RphY8ipNtQNOXxeBITE5ubm1tbW1NSUuKvJzQchWau8ckOKSkp6enpNpsNan8WFhbC68Fg0Gw2a5rW3d0dDAbD4bDf7w+Hwy0tLbAWxuFwQExBU7W0tDS47F1dXdCwE/qAQmStpqZG1/VezXF8Ph/LsiaT6bPPPotGo9DBFNIoCILYt29fOBxOSEg4fPgwhDtxiQRCCCGEEEJHO532lpqqURQ1bNiwlpYW+JveWDoBoKh+MBiEZ4ZnAuYq8XsmCGLMmDFDhw4Nh8PffPPNsmXLKisroVB/SUmJsQ082CdJUtd0nfzOhNDhcIwePXrjxo1VVVX79+/Py8tLSEhYunRpcnKyx+OZMmUK8W1GBhG3GARmp0eGoekEdaRQQvxoT/CU+6GHHurp6RFF0WKxQAVHp9P5xz/+8UwCDaenoaEhNTU1FostWrTIWA4Aj5ePTGspKiMj4wT1AiBWkpubm5yc3NTUVFtbO3jwYEVRqqqrfD7fDTfcAO8dP378J598snHjxmnTppnN5tra2qamptzcXGM+yfP8wYMHzWZzIBh48skn4cVegYbs7GxRFIPBYPzKlF7XmSKpMWPGrF+/vr6+/r333rv44oshT97v91dXV5eXl3u93mnTplEUBRk8oihC8CW+iOmJl9zHH9pwMpNMuOl2uz1+QguhDeN/k5KSIAWju7v7e3d4AgMHDtR1PRAI+Hy+nJwciqL279/f1dV17bXXwgY///nP33zzzerq6uk/n04zdH19PSQvwGxflmVBELq7u2ma3rt37+7du5OTk3mePxJooGlZlp1Op9vtdjqdPp8vFos5HI7vrGSJuyAMzcTXK4EOkT09PfCBh4wAKDICel3McDgMCSBGphIwmUx79+7dsGHD9u3b9+/fzzAMxIwyMzMh98FY2wVHHz9+/OrVq0tLS/v16zd58mS3202SZGdn5+eff75161av1zt58uT4/ft8vkgksmnTpnXr1nEcxzAMwzBGVMVms3m9XkVRYAkVRhkQQgghhBA6ptMJNCiqwrLshRdeWFFRcfjwYXieH59IDAsNaJqGSXvfgn4BFEUlJyfPuHSGy+V65JFHwuHwu+++C4GGE9N13eFwjB07Nj8//5tvvtm7d++QIUPC4XBNTY0sy8OGDYMEh9MGVQOOlpqaynEcZDQYgYbU1NQzOZZRLNOoVnDy3G73Nddc43a7eZ6PRqMcx8GsSRCExMREVVVPECRiaAbOaPTo0Z9//nllZeWsWbMaGxurqqo4jps2bZqu65IkXXTRRdCp4dChQzk5OdXV1aFQqKioCJ7kEwQRi8XgibHNZrvtttvgxV6BBrPZzPN8//79HQ5HfOJM/LSWoqni4uIrr7xy8eLFL7/88sGDB5OTkx0OR319/bp16zwez8UXX3yGH0XjoGeykx9amjdt2LBhe/furampGT9+fHNz8/79+y0WCxQ3JQhi4sSJS5cu3bVr1+Gmw3l5eVAyY8qUKcYdAaIoDhkyZMaMGTRNQ6AB4lCQA+JyuTRNGz16NLxIEATLspD48COc48aNG1944YUDBw6MGDHi1ltvNZvNDofDbDa7XK7du3f/85//jO/MQhDEoEGDbrrppuXLl7/yyit79uzxer2apjU2Nm7fvt3j8fz2t78dOnRo/P6tVmsoFBozZszs2bNjsRj8HpNl2WKx+P1+lmUzMjIikUhCQgLLsCcZnEIIIYQQQuh/zSkHGqCUPUmSxcXFbre7paVF0zT4W9zYBhLX3W735ZdfftojOzpfANA0LQiC1WqFvPpJkybl5ubu2bPHKBYA4AH40TNwWZY5joMagWVlZVVVVddee+3OnTslSYpGo3PmzDmTAWuqdrwJ/3333QdLJ+D5LSyd6FUY73iMh7S9rommabCI4JhzPFgmoGlar4ICeXl5HR0d6enp8+bNg3m+ruvGRFHTNF3ToTvDccejqaqk6rp+/vnnr1+/HooCwMqI7Oxs6DdhtVjHjBnj9XoFQaipqUlJSWlvbzebzdnZ2UauBE3T2dnZuq5nZWXNnjXb2LmiKIqi2O12qAtoMpl0XYcrdszxQIrNvHnz6uvrV6xYsXnz5kgkkpycnJKSctVVV82ePTsjI8PYGJqVgGOWFzkmuDhwGU+pGEdSUhLc7hNkK/T09AiCkJCQ0CuN/1TJijx58uS1a9cePHiQIIi1a9cGg8Hs7GwjjuByuVwuV0tLS3Nzs91uNzIaYOEPx3HwYxsKhXJzc6+44gqoJdErVcFI5YhfwHL0lJtmaE3+zwawhCQpKQnKuxq3QPxWr+KODocjFAqFQqEBAwbE73bp0qWHDh2aNGnSAw88kJqaagRBCIKIxWJ2ux0qTRDf3qbU1NRp06bV1NSsWrVq7dq1NE1bLJZBgwZNnz69pKRk0KBBMB5j/0lJSbFYLDMzc+TIkbBciyRJOFPoCQrHCofDJ/4ZQQghhBBC6H/ZKQQajPkVTdGwzvmWW255+umnGxsbeZ6nKMp4Ou33+9PT0/8/e+cdXlWV7v+1++nnJCe9kgRIoYRAKAYQlc6gaATxIhZE5w46o2IfRxzHGe9jxdG5iv5wQFFRQEA0VAFBKaEpLYVAAuk9p5fdf3+8sOdMmgGCeJn1GZ95wil7r7b32e93veWBBx5oV1LuooBHfIip1uv12uuQ3g9qBIB5A8LB+UoQqhIeHh4IBPx+vxYIEHpYsJFsNltubu5XX31VXFwcCAR27twpSVJubu6QIUN66BIviiLYvS6XC5wsaJqWFZmhmE4/Hx0dfVH+C5AHARLjKYpiMplkSW7ncq/lp+yhwQzDBd4BLpdr165d4ANCURTkeoCBBcO+m0GgSEpWZIIg0tPTeZ6vqqo6duwYLIPrrrsOXA8cTgfLsunp6Vu3bi0tLU1LSysqKhJFEcqUQmMMBkN8fDxBEJWVlafKTiUmJppMJoqmWJaVJVkQBMjyiBAiCIKhGWiV+u8QBKGoik6n27dvX2FhYV5e3r333hsXF6eqqsVigSMQBHE+awCUt7wgvmg5L392xrUBgZQNFEV5vV6z2fyzYw7VDUpLS0+ePBlq02pnDAQCx48fNxqNERERffv2/flZ7Jbc3FyTyXTy5Mni4uLm5maHwzF37lxwTiFIYsCAAePHj//666+PHj2q1+srKysjIyMHDx6MELJarZCJYMCAAYqilJaWnj59Ojs7G3JAhmZeBEkRdAEYFrPZDLv9VVVVSUlJWmMoioJulpeXEwQRGRkJuTBRSATKuXPngsEgQgiqtGq1PwVBqKqqMhgMRqPRYDDAiQ4cOPDTTz/pdLoZM2YkJibKkkxSJLjPsCxbV1cHCSPaZev86aefCgsLb7nllrlz5/I8bzabKYqy2+1QYQTUOm06YmNjW1pajv50NDIyEjrO8zzIQEEhCI5IgiDA1HdzmbRbpdrrv3KnGAwGg8FgMBgMple4aL9fgiAg/QFCaNiwYX/+85/Hjx+vGV2Q9H5I9pA//OEPUDXwklFkhed5hmZAZVBVVZZkURQVVWHo87bK+bT2shwMBvv166eoCrjcJycnQ364jiUnIekDTdOpqalpaWnl5eUVFRVFJ4vMZjNEa/fQEoD8fKIoQmK8xMTE3s0MB7v0HMcZjUaSJB0OR8cdVDCYO22zqqqKqnRq6vTt2zc8PDwsLGzjxo1msxlyRoDVDSJOMBiEQgMdi4MCULESIRQeHp6VleX1eouLiysrKz0ez8iRIzmOMxgMOp3OYrGMHj2a5/na2tqffvpJFMX09HQIkocjsyyblpam1+vr6+u3bt1qNBpBWoJcmzqdDlSG8yUGlfMpLWVZhtgcRVbgFYZhqqurv/322+rq6htuuGHo0KGxsbGQQsJmsxkMBpqmCZIgSEJRFPDg6DgsPzsd0GCj0chxHFRA7MkX3W735MmTOY47c+ZMTU0N9C4QCASDQdA+2tratm7dKssyFHfsYXu6wmw2Z2Vl+Xy+I0eOlJaWIoQgbkLr6ahRozweT11dXWlpqdfrzcjIAGFIls7ndExLS4uJiXE6nZANFDIjaiqD5tAUuvA8Hg8khf3+++9dLpcoivCuz+eDkqXffvttY2PjoEGDTCaTx+NBCIFYQ5JkS0tLVVWVTqfTsn76/X5RFMvLywsLCzmOGzp0KOSP8Pv9TU1Ner3eYrFkZGQghEDtkmUZ8jgcPnwY1m3ogLS2tm7bti0QCEyePDkjI2PggIGpqanJycngTGQwGOAI2udzc3MDgUBzS/N3330HqRk4joMsoXA9IoQgBgpd8DC62LWEwWAwGAwGg8Fc81yiYaN5U2dkZDz55JMbNmx4+OGHZ82atWDBgg8//PCzlZ/dcsstoQ7JlwBFUxzHKari9/v9fr8gCARJQM0Iij7v0S1L8ooVK44dO8bzfE5ODmyV22w2SLtw+vRpv9/fzgAgSRJs+LS0tEGDBjEM8/LLL0PWicmTJ3dlWnfaPFmSm5ubz5075/f7c3JyIHXc5XQ5FEVVaJo2GAw2m02v1+/du9fhcLQrxacZe+3o6piwBW2xWG699Va/319SUrJy5Uqv1ws7z6BN0DSt0+kEQQD/9i67T1EIoYiIiLy8vLa2tj179pSUlGRkZGRlZcG3ID5i1KhRdru9rKwMjL3hw4fHxsZCmUn4WHp6+qRJk1paWo4dO1ZSUoIQAntbURSe5/1+fyAQYFnW7/ezLAsrSksfCMsA2iNJUkNDgyAIVqtVlmXwBNGqXV5m3j5BECRZQgjp9fqIiAiCIAoLCx0OR0++q9frb7nlFlmWBUFYvnw5x3GqosIgUzQVCAQ+++yzmpoajuOeffbZy80PQhB2uz0vL8/pdO7fv7+2tjYzM1MLioG1kZOTEx8fX1RUtGvXLpfLNWbMmPDwcIIgAsHzWV3T09MnT57c3NxcUFCwb98+qO8IDiboQlHJdpc2yHnQF6/XyzCMIivBYNCgN1it1vXr1tfW1iKE7rjjDoQQKJKSJEE+SFmWP/roI1BeIBWCwWBgGObIkSOHDh0ym805OTma5whcC4IgQA1OKODKMExERMSuXbsOHjzY0tJCURQoVjqdTqfT+f3+uro6QRBaW1sVRRHE81cQeAwpiqKtK2DGjBl2u52iqOXLl2uaBUTuUCTFMAxIIeD1czmThcFgMBgMBoPBXMNclmEMPgU2m02n08EeI0IoEAhIkiQIAqQMvEwYhtmzZ88rr7wybNiw7Ozs7OxsmqZjYmL0Ov2RH4+sW7fuwIEDPM+PHDly5syZECgBzuo7duyora2FzdV2RiZBEuDUkJOTs23btpKSEkEQ8vLyYqJjoHZduz521XdJllwuV1NTE0mSucNywaOht8wPaDZ4NNA0vXPnzkmTJg0ePFjTMrpRAcDlu1PRBIbivvvu27RpU3l5+QcffFBZWXnHHXekpKRAIMbx48c3bty4Z8+elStXtssRGArUO5QVOSsry+PxgOH3wAMPaDYbSZIej4ckybFjx27btq26ulqSpPT09NBwA1VVTSbTAw88cOTIkdOnTz/00ENz586dMWNGVFSU3++vr6+H7WhBEP72t791n3ivT58+ffv23bt375///OfBgwcbjUYwsO12e2xsbHh4OBR0RF3ls7gQhdHpwbUKlzqdLiwsTBTFffv2nTx5csyYMV21RwM8At56662nnnrq66+/DgQCDz74YHh4eJgt7GTRydWrV2/dupWiqLy8vMTExJ892s9iMpnAx6SoqKihoWHGjBkJCQmha9JsNufm5u7YsaO6utrr9WZlZcGUBQIBk8nEMIyiKNOmTdu9e3dxcfGf//znOXPmjB8/3mKx8DwvSdKpU6cOHDhQWlq6fPlybbgEQaAoKjIysrKycuHChQsXLkxLS7OH21taW9asWbN27VpVVUePHj1+/HitGTRNkyRJ03QgEDhz5swzzzyTn58/dOhQhmHKy8sLCgpWrVplNBrHjRs3evRohBDLsizLDho0iCTJ5ubmf/7znzabrW/fvpGRkQ6Ho6Cg4KOPPoqPj/f7/S6XC8QRcDqw2+2pqanl5eX//Oc/v/76a6gAQlGUxWKx2WzDhg2bOHGiNj4EQVit1r/+9a+LFi06derUgw8+OHfu3IEDB6amphIEUd9QX1hYuH///qSkpEcffdTn88ECvvxZw2AwGAwGg8FgrjEuQgtolxAOhaTiD82hEPr3ZaKlTqyvr//mm2+2bdsGL4IFDn+zLDt48OB//OMf8Ao4xg8YMAAhdOLECVA9wANfa7nW7KlTp65Zs+bEiRM0Td91110kRZqMJvTv4oIkSZp8AAkjoCwfTdMcxxUVFUGQ+YiRI1iGRb0Xgw0d0el0d911V3FxcU1NzcMPP2yz2URRjIiI+Oijj0AF4DiOZdnW1tbQNmt5Fpqbm81mM7i7Qxw7fMBgMKxcufKZZ54pLCxctWrV6tWrQ8dHFMWYmJhuVAaApEiKpkA7kGWZYZj09HSGYVRVlSRJlmUoB5CXl7dx40aEUL9+/TRbGoxbUKOSkpL+9re/vf/++4cPH/7ggw/efvtto9EILgCSJPE8P27cOEgfQJKk1+uFxAqyLEM5Rpqm9Xq9IiuTJk06dOhQWVnZ0aNH4SwEQZhMJlmWHQ7Hio9XPP7E46NHj5YlWZIkRVH0er3dbu/Kh4UgCKPRyPN8IBDQ6/UQKWOxWKZMmVJYWFhbW/vYY48xDBMZGVlfX79x48ZuEnBAOMNTTz31+uuv79+/f+vWrbGxsW63u6WlBTbP77///gULFhAE4fV6Q6uK1tXVQQ0O1LN1xXGcLMl2uz0+Pt7pdNI0PWLEiHYCjdVqzcnJ2bRpk9VqnTBhgtZsKAiKEGJZdtTIUYsWLVq6dGlNTc3ixYsXL14Mb6mq6vV6Y2JiBg8eHNoeCCeZN29eXFzcokWLfvvb38LSVRTF6XQajcbhw4d/8P4HqqLKyvlMGaDKtba2vvTSS36/f9WqVfv374cECgghj8djt9uvu+663//+96EdtNlsjzzyyBtvvFFTU/PQQw/ZbDZVVQOBgNPpvOGGG/7617/m5+eDuwGUwoFvTZ48uby8vKampra2Fk5BkmQgEOA4btmyZdnZ2QsXLhw+fDisSVEUhw0b9uijjz7//PMlJSUvvfQSQojneY/HExYWZjQaKYrq168f6DJQ3LdTPB4PRVGCIIROKE7QgMFgMBgMBoP5D6HXXP2vBGA4paamPv3004WFhfV19W6Pm+M4WZZ5nrdYLFA6IT09HVzlEUKgQaSmpubk5Bw9enTfvn1z5szp6vgMw+Tl5TU0NBgMhrS0NIIg6vmJEAAAIABJREFU2mVbRAgpiuLz+ZKTk6HUBUEQsLkKVm5FRUVzc/OUKVPsdnvvZqGHMAeSIFNSUp588slNmzYVFhaGphiAEbDb7Xq9PrSqggZFUYmJiREREXFxcbDDH/quxWJZvHjx9u3bN2/e3NLS0tDQgBCKiYnhOA48R362hWA10TQ9adKkM6fP6PS6IUOGGAwGqBkBxi1FUWlpafHx8aqqpqWlaWYtvAtpGilE5eTkvPPOO59++umOHTsIgoAsgOHh4SzLDhs2LD8/H9wTVFVlWTY2NjYyMlKv14MPPyTzW7V61VtvvUWS5F133aW1ENKCVlRUnD59+lTZqXXr1hmNxv79+5tMJqfTmZaWVllZCTvSHUNOoECm0Wg0mUyhhvqQIUOee+65xYsXu91ur9crimJCQkL3IQ9QivWGG25ITU3dvHnzkSNHfD5fdHR0ZGRkcnLyvffeO2zYMJ/PZzAYTCZTqPCRnJzsdrthafXQU4aiqcjIyMzMzOLi4oEDByYkJLRzA/F6vTfddNPatWtJkhw0aBCEsWgJGuEzsiLfdNNNQ4cOXbZs2dGjRwOBgNvtDgQCVqs1LCxs+PDho0aNCh0uiqJMJlN6evqAAQPee++9tWvX/vTTTxaLJRgMJiQkTJ8+/d57723XTk28a2lpeebpZ4YPH/7FF180NjZCBof4+PgRI0Y8+eSTHWWgCRMmxMbGvvbaa6qq+nw+iqL69+/ft2/fBQsWmM3mcePGnT592mq1QvOampqWLVv25ZdfhoeHT5gwwW63a8kg6+vrHQ7HwYMHKyoqPvvss/j4+OTkZEhUwfP85MmTc3NzX3/99bq6OrfbzXEchLekpKSkp6dPnDgR5NRukrBColOv1xseHq51uScziMFgMBgMBoPBXAP0mqv/FQKcGqDgAkJIluTaulpVVePi4kIT1EFyePgkSZGCIPzjH//4+OOP8/LyXnjhBbDD1Qs1C4gLpQqhrEMwGNR0CqBTL/pQjwD4zPHjx1/6y0unyk69/vrrU6dOvRLdV1UV0hCSFOnxeFpaWqxWK4Sghxq3UJNSq6yp1aeECqCddgc8F8CDPRgM1tXVIYTi4uLAcaCH+TUURQGPD/hn6LdgOsC7QbNjFVkhqU7SJUC5EO3r5eXl4A0BBRcVWaFoShAEiqTaqTngXfLNN9/88Y9/zMnJeeutt0IDECDThMfjaWxsvOeeewRBWLhw4R133AG9hoySnabwhLcgvqajBgGrEZaiKIppaWk9HCsQj1RVra6uNplMNqvN6/NCqA68GwwG/22NKaqsnM/RiLqwVNu9CKqBdq7Qjf2OHwtd0u08YuB17cryeDx+vz8pKQmur9CTavVQeZ6HFI/gbCLLMvie6HQ6juNCJQOSJAVBWLZs2V/+8pff//73jzzyCEy0IAggNJjNZoIgOl2Emizicrmam5vj4uIMBkMwGBQEQafTwbegSSRFvv3220uWLJkwYcLChQuhokc7/45vv/329ddfb2hoWLJkyejRoyEBKk3RFE3BXMCk1NbWMgwTFRWl0+m8Xm9ouEQ3FSUgqwVUwcAqAwaDwWAwGAzmP4pfr0cDPMGDUaGZlxRNQf08nuchboIgCFVRVUKFhPMkQYIRPnny5MLCwsOHD1dXV4PQ0M7KBRNIVVWKojpqDaEEg0GIUGjXvOPHjx8/cXzAgAFafopeB5ws4G+z2Ry6/Q52O7xLEISW1pMgCII630dw2yYIIhgMMjQT2n2tdiZCCLZq0YV9Zp7neyg0gF0XakSBHaiVIKVpGg4FBl6nTh+qqoqSCJoFfF4z3eFFWZYJkmBZtp2NB++qqvrll1+mpqbOnj07Ojo61PCDKTOZTF6vd+TIkZCQEl6EkopdTTpJktB+n88XmlQC7Gew/AmSiI2NBSEDkjuibnetwb6FKQMHmUAwYLVaW1tbOY4zmUywzELbD1VUEEKyLPewgimMtpaho1OVQZbkYDAIsQPogm4S+gFFUeBFUIsYhgkPD7darTzP0xQdqtad7xeJoO+QFRI8TUBSgV63CwkJxePxaEVGZVm22+0IoUAgoF3+7SYdTgEyWd++fSFjqLYOaZo+n6AEKeXl5Tt37qRpevbs2VrdUFVRobXBYNDr9aakpMTFxbW0tIDAoa1Vv98PCURhncCClCUZCs0KgiAIAuh9XU2EqqoUSUGBHqwyYDAYDAaDwWD+0/j1Cg1gz3SV/0+zoFRVJSlSVVXIdQ+7+qqqDho0KC8vr6ys7NVXX33ttdciIiJMJhPZocoGmCjt7Op2hgHYkOdL2Cnn7cCS0pINGzZERkY+/PDDCQkJvdHjn6Hj1jp0vGObQ01BrZCkLMskIrXXQ23sdoftee2DjhYU2MMESVCIgrHS0nmoXVTi1BQfzeVEex2MPS3/pWZwap/hOE6SJKjHaTKZfD5fWFiYdmRQPUiSBH94CJBpaWkJs4UxNAPVLruq0wHuDCaTKdQFhqZpaAw0gCRIWZKNRmPotzrt47/G54JJr40/mNaqqsKwa9+FQ7EMK0s9VRna2/9dfIakSGjz+UuMav9JiqJgcEJXAkVR0AxoWOi5NFEgVHUKHdh2ISGhgKZDkRQKSe8SesUZjcbQc2mfCQ8PBz0LegpJNMD9B5HnI49cLldqamowGIQAKLPZHDr+JEk2Nja2tLSwLBsZGRnaJE0WgRGABmjf1ZQUTfLr2K/Qz2MwGAwGg8FgMP9pXGJ5y1+Gnu8EwifBZNKe+2fNmjVmzJhTp04dPHjQYDCAVdlbbVu/fn1xcfGMGTPGjRun6RS/ZBxK930B+0ezhVC38eS9yHmrjyQJguhowV4OXY0tTdOxsbHNzc2lpaVg0CqKEuoOU1tbu3Tp0oMHD0KSCIvF0sMjX2zzen32NdWmKzWk02ZcwonafavnusYlnOvS6HgucJBhLkBSpPYffJjjuJiYmHPnzp07d66dZwrg8Xi+/PLLsrKyUaNGgT4lS3LPm6RdYpfXMwwGg8FgMBgM5hrk1+vRcGlo9lgwGExMTFywYMHUqVNJkoQI8F44+AXbOTc3Nzs7+/rrr3e73ZCxH0diXyaXPHrTpk2rq6vbtGmT1+vNzc21Wq0Gg8Hn87nd7pqampKSkm+//TYqKmr27NlZWVmoB34Hvx5AZej9w/7ifW93RnBX6aZe6eUcXxAEmqbDwsKmT59eVFS0bt06j8eTm5sbqjUUFxd///33hw4duuGGG2bMmBEbGwuVTShEdXVYDAaDwWAwGAwG00OuNaFBQ6fTeTyePn36ZGVmgQ8zwzCQNaBXjIfJkydrVSQRVhmuKjfddFNLS8tXX31VUFCwdetWu93OcRzkU4AshuPGjcvPz7/pppsQQrIka1kzf820S4WA6TksyzqdTpvNNnXqVKfTuWbNms8//3zTpk2hQS4+n6+1tXX69Ok333zz4MGD9Xo95DG5is3GYDAYDAaDwWCuGX7tVScuDc34h3+CtdZNmoCeH7bTnAih/Nosw67m99fWTnSRYk27fkmSVFdXt2PHjqqqKu1Fu92ekpKSmppKEERmZqb2esciDpd23q4qDvTW2Pbk2ry0c3V/IVy582oEAoGDBw8uXbp00qRJc+fORRfquV6Jcx09evTw4cNNTU0+n0978brrrktLS4OithBi07GgBgaDwWAwGAwGg7k0/oOEho5/X8JhsdBw5bgcoQGQJElLOgh5DSiK0lwDtINjoeGqCw2A2+3WEnx2c8DLPJfX6+U4jqKo0OApSCMC2Sh6MYIDg8FgMBgMBoPBoGtVaEBXzMD+2eH6tRnw/4eEhouiXb+6qhwhiiK8S8L/SBJ1LRBc7Hn/7woNV/28qqqCKkSSpJZ78gqJd3CWjq9D6seLyrWJwWAwGAwGg8FgesI1m6MB8x+FZhuH7tUTBMGy7NVsFqYLQquWoitWwOJ8FVKSVBRFkiSapiFAA+rUKqoCRTExGAwGg8FgMBhM73LNejSgLkInMNc2l+OtcDnnCgWvt55zpb0nOl0PXeVwueSzYDAYDAaDwWAwmFDwhh4Gg8FgMBgMBoPBYDCYXuNaDp3AW5QYDKYr8P0Bg8FgMBgMBoO5QmCPBgwGg8FgMBgMBoPBYDC9xrWcowGDwWC64qLKqWIwGAwGg8FgMJiegz0aMBgMBoPBYDAYDAaDwfQa2KMBg8FgMBgMBoPBYDAYTK+BPRowGAwGg8FgMBgMBoPB9BpYaMBgMBgMBoPBYDAYDAbTa2ChAYPBYDAYDAaDwWAwGEyvgYUGDAaDwWAwGAwGg8FgML0GFhowGAwGg8FgMBgMBoPB9BpYaMBgMBgMBoPBYDAYDAbTa2ChAYPBYDAYDAaDwWAwGEyvgYUGDAaDwWAwGAwGg8FgML0GFhowGAwGg8FgMBgMBoPB9BpYaMBgMBgMBoPBYDAYDAbTa2ChAYPBYDAYDAaDwWAwGEyvgYUGDAaDwWAwGAwGg8FgML0GFhowGAwGg8FgMBgMBoPB9BpYaMBgMBgMBoPBYDAYDAbTa2ChAYPBYDAYDAaDwWAwGEyvgYUGDAaDwWAwGAwGg8FgML3GtSA0qKp6tZuAwWAwGMylgH/CMBgMBoPBXHsQ18YjjiAIqqqSJElRFEleBfWE53mEEMdxv/yp/9MQRREhxDDM1W4IBoNUVSUI4mq3AvN/EkVRRFEkCIJl2StxfFVV4fj4bonBYDAYDOaX5xoRGjRUVVVkhSCJX0xuwJbGVURRFFVRSYrEU4D5ZVAURZIkhBBN01dF08RgMBgMBoPBYH79XCNCgyzJiqrQNA0GJ3TqlzQ+BUFACF2hjSlMKIqiyLJMEiRFU1e7LZj/XOCeQxIk1rkwlwbcytAV888C2R0hhG+VGAwGg8Fgfnnoq92AXiAYDDY1NTkcjoiICIvFYjabf/nn/vLycoRQZmbmL3ze/xA0txGe55uampxOp8fjsVqtUVFRNpsNOwZrYP+aX4BgMOh0OhsbG10uV0JCgtVqDQsLIwgCj/z/Ca76NaI1gCTJEydOhIWFRUdHdxp2pyjKJXvNwFmKS4ptNltUVBQO68NgMBgMBvMLcy0IDTqdbvXq1SdPntTr9UuWLEEIKYoS+ih5pR8rHQ7HG2+80dzc/Nhjj910001X9FxXGlEUSYI8feY0SZJxcXEMw7Ase9UtKEmSZFlmaKa4uPitt95yuVw0RZMUyXHcXXfdNXXq1Ct6dlmSCZL4NcdoBAKBsrKyPn36mIwmiqZUVYWUJVe7XdcmK1euXLNmDcuyoiiyLJuSkrJgwYI+ffpgh6auEEVRcze7Kvj9/tOnT1sslpSUlKt+CYME4PF4Xn311YMHD86dO/fOO+90u90WiwUh5Ha7y8vLjUZj//79YYFdWoNVVT148ODHH3/s9Xqfeuqp/v37Y60Bg8FgMBjML8m1YIqoqlpbW1taWvqvV5RfNB7E4/E0NTU1NTV5PJ5f8rxXAoZhKs5WPProo0899dSxY8c4jgsGg1e3SRAIoyjKhq83vPTSS5WVlYIgGIwGQRAOHjzYt2/fK90AcDymaAoCZH5VKIoSCAT+/ve/v/SXl/72t78pqoIQgt119QJXu43XAl6vFyEkCMLbb7/94Ycfut1uvV5vt9sbGxtZlrXb7Vhl6BSXy4UQYhhGkZWruBQrKs7f044ePYoQCgaDV7ExoT9PNTU1LpeLZVlQGQRBqKmpeeyxx956663q6mqGYS5ZFoGzlJSU1NbWBgIB7PaFwWAwGAzmF+Za8GiQJMnn88ET7dXC7XZfAyoDQkgUxcrKykAg0NTUdLXb8i9IgvT5fAcOHDh37tyNN974wgsviKJoNps9Hk9kZOSVPrvf7xdF0Wq1/gqNSZIk9Xr9li1bRFHU6XU0TWNl4UoAo3r8+PF169bRNH333XfPnz/f5/PRNI0Q0uv1V7uBv1KsVqvf7zcYDK1trXAFXRWHgnPnzgUCgcrKyl/+1N3jcrn8fr8WIsGy7OHDhz0eT1lZmdvtTkhIuPzjcxx3OSEYGAwGg8FgMJfGtfDwQdO0yWQKCwuDxFoIIVmRf8kGmM1mkiRJkjSbzVf6XFfajGQYZsKECYMGDRo9enR2djbP81fdiCIIQlbklpaWQ4cOqap6991322y2yMhInU4HKoOiKBd7zIsaxieffPKhhx5CncXgKIoiSz1dbHDS3p1BKPb57LPPRkRE3HnnnQRBBINByAB3Ua3CdATWFYwPXNp79uyRJIkkyfvuuw9e5Djuql8gvwwXu060q9JgMJSVlc2YMePAgQNQh/gKtK492lngj+nTpw8aNGjMmDFDhgxBCOl0uks7Wq/DMIyqqjzPB4NBGLFx48aNHDlyzJgxUVFRPM9D4eRLJiwsDH6eFEXB/k0YDAaDwWB+SS7XowHC12G3BB6JYM8KsiT04uZVa2sry7Icx1HkeT92rQG8wEuSBBbXFcXr9bIsy7Ks1+ulKEpVVYPBgBDy+Xwmk8nj8QQCgdDPOxwOhmE4joNtTxgNeJokCEKSJFVVQ4vkwVMgxOXyPC/Lsk6ng3e1Z8TQIVUUBcacoih4q1P/WJ7nSZIMBALgnQvHCQQC0Ph2BAIBvV6/ePHidgG9giBo+/mQb5+iqNBdMuhXp/tm7Vqu5VqHYdHe7XS1yJIcCAaMRmN9fb3L5YqNjbVarfCWKIoMw0Bj2gUza+nWoBhhR08EgiAgDoIiKUhqIAgCx3E8zzMMo425KIqCIPzwww/Z2dmKoni9XqPBiBCiaMrpdNpsNphEWI2wFGmahtMJgtBucqFVqqpKkhQ6ejzP+/1+q9UKr3i9XlVVeyhawYzn5eXdeOON50dMlil9J0nmoXk6nc7n8zEMo9PpYBnDRP/spQqjAcPV1Wc8Hg9FURRFtZsLRVYkWYI56jgXWkYJj8djNBphEARB0O4hmgM5zKbWhtA1qSgKGLE0TffESxy6o6oqTHfo6lUUBTaZdTodTdOqogaCATAIIenshAkTKIoKBoMEQYDKAHPaQ+90URR9Pp/RaITPw+UJKQygg9qyaZe20Ol0Go1GmqZ5ntdMZRg9OPtF3W+1XW645OGfnSZKhPQBsHQ9Ho9Op+vqVoMu1N+BCdUOuGvXLlEUYXddFMV2tw7tpFqeAoQQ6HcEeT5pYugp/H6/Xq8nCEKWZIqmAoEAy7AUTfn9fpqm4QeCpP7t1kSS5Afvf0BSJIyVoig0RWsrGRYSz/OCIMCloV28XZUT6ng3BqWApmmapuH+1q6bnQJXBEEQLMtKkiQIQkJ8wmuvvabT6WCEQ3VM0B2ghaGnFgRB+6d2UoIkCIKQZVlbIdr9tpuf5tDfHRjedh/o9EUMBoPBYDCYjly60ACP5tozRzAYhGB+eII3Go296yJrt9vh4IIgyLJMKzQ8+lM0RYgEOJBrj4Msy16JrRuTyeT3+1mW1ev0CCFFVYLBYDebY2FhYfCHIAiSJMGASJIENiQYyaiDpQ290PQar9cLT+cMw2gP9/DsKMsyPNFyHNfpEy08LnMcpxmuqqoGg0FBEECbQAh5vd7QyQLDieM4r9dL07TWO+2x3uVyWa1WJCFBEBia4QVeFEV4UGZoplMXGdhmV1UVbFp40If2ayZWV6uFoiloHlgLYO0ghAKBADxGEwRBUzTD/cvsgRUoyzIMC03ToQaMZtXAOGu6j9vtttvt4GYMD+48z3Mct337dkEQgsEgSZIWiwX8wP1+PxyNZVnNEqBoykAb4ELQ6XTtjBPoI8yjJEl+v99kMqELuhK8BTnzwFbvdDQ6RVEUq9UK84gQgv/3eDwmkwl6CpoIRVNyQNYMD4QQNKBTDavTSQy16mHS0QWTEiz2juIIzIWiKBRFwelkSW6XUxNGJhAImEwmVVUDgUAwGLSYLZD+M9SqIUkydFRhUTEMA+HosFbBx+RnbSHojizJUL4EBDuw30iShKZCN2mK1v7Z3NzMcRy0wWAwuN1ukiRhVZNET73DVFW12WzaPzmOk6XzkyKKIs/z2lptNyMWiwXEFOipNrbasHR/O2rXBu2OIYqiXq8nSdLr9UJP2wHnhVUKq9dgMIR644OWJEoiaDcWiwXUBIQQz/Mej2ft2rUwtoIgUCRFkqTW1FBpw2KxgPgly7IkSdo4gIgMdrIiKxzLSZKkvcLzvNfr1el0ZrNZkydCb6qKrCASyYrsD/gpigL9SJIkMSjCuaAlHMfBVLpcrkAgQNN0N9FS2j0NZIXzChfNIIRURYUbtaYt/ux0wEgyDCPLMkVTBv2/JGBFVShEud1us9kcCARg5Wi3OLgxiqIIY6WtZ2iGxnl3hgu5IVSkKqoC1w50RFs2mkQIWht0iqKpni8tDAaDwWAwGOBShAZ4eILdHpZl3W53ZWVlbW1tcXExQigrKysxMTElJcVgMLS2toJA0Ct4vd7Gxkan03ns2DGPxxMdFT1w0MDk5GSz2RweHi4IgparDzameuu8Gqqq1tTU1NfXFxcXezyeESNGmEymlJSUbr7i9/sbGhqcTmdtbW11dTVCKDExMTIyMjo6Oi4uDiQA7eCnT5+ura0dOnSo1Wr1eDxnz549ffp0W1tbVFRUSkpKWloadArGweVytbW1QQrMjIyMrKysiIgIeCwGQ0tRlKamppMnT6ampvbr108QhLq6usbGxnNnzzU0NmRnZ1ut1uTkZLvd3i4nvNPp3Lt3r8Fg6N+/f2xsLEmSYB4fPHiwtbU1Ly8PNifLy8vPVpx1e9yKosTHx6enp0dHR0PVg3aDAJ8vKSk5ffq03+83Go02m81oNIIlbzAYOI7LzMyEXdN23wUrwu1279mzx+12GwyGAwcOVFdXw98TJkyA41OICgQC0EGHw9HU1ORyuTIzM+Pi4tLS0jSLBV3wZYCBcrlcPp/P4XCcPXu2urpaVdXk5GSGYbKzs202G5Qw3LRpE8MwwWCwoKAAVACWZbOysrSFrahKwBugabqqqurs2bMtLS1OpzMiIqJv3759+vSxWW2hAwKOMPv37w8EArm5uREREYFAoLy8/PDhww0NDUlJSVOmTAkLC+t5MgiYa1hdJpMpMyNTVmSKpMxmc1NT07FjxxITEzMyMnier6mpqaurKykpoSgqKioqMTExMTHRZrWhC5ufPTmd2+2GEa6rq9PWc1paWmxsrN1uBzOSoime5xsaGmDlw1z069fPbDYnJCTEx8WHngtuJi6X6+zZs+fOnauoqOB5PiYmhmXZtLS0tLS0jsae1oYTJ054PJ6oqKjBgwdbrdbExESEkCzLJEH+bFA6z/MsyzqcjoqKCr/fX1JSghDKzMyMj4+3Wq02mw10MZCrEEIej6eoqKihoQEc3Xfv3o0QcjgcKSkpOTk5KERy7R4w+bxe77Zt2yIiIq6//nqEUGVVpd/vP378eFNTk9ls7tevX3x8fFJSEmipoYIOGHuCIBw+fFhb54MHD46Ojk5OTu5UJui0DaANBYNBuJl4PJ7i4uJgMGiz2VJSUpKSkuLj/22aQI9raWmpqKhQVTUnJwc8PuBdgiD8Ab9Bb2hraysqKkpJSUnpkwJeVGVlZaWlpVVVVVFRUdXV1Tt37oSt++jo6KysLIZhQLEiCbKmtqa4uLiurs7hcFit1oyMDC0UDiFEkuTgwYNtNpsgCiRJ0hTNC3xra2t5eXlzc7PD4VAUJTs722g09unTB9RSTUMkSCIQCGzevDkiIiItLS0uLo4gCIqiGIY5c+ZMUVHRsGHDEhISHA5HTU2Nx+M5evQoTdPR0dEJCQl9+vTp9Ccs9J7W1tZmtVpjYmKsVqvb7dbpdHBPy8jIgBnp4cVFEARN05WVlYWFhf369Rs6dCjImi6XS5Kkffv2iaJ4ww03aL8pJ0+ebGtrCwsLS09Pj42NTYhPoOh/aZQUTWmqJUVRNPVvv/U+n++nn34SBCErK8tms8FUgo5WUVFx5syZtrY2nudtNlt0dLTVarVarf369qNoKtRVBIPBYDAYDKYbLtGjQdtGq62t/fjjj5cvX04QBDyR8zyfkJAwf/78GTNmhBp4l3MiOOy+ffu++OKL3bt36/V6m80my3JTU9PUqVMffPBB2KHSjHbwnu111qxZ8+6779bW1kZHR7vd7pUrV0qS9MADD8ycORM+APtsGoIg7N27F9qMEIqIiEAItbS0WK3WqVOnzpw5MyMjIzRC4b333tuzZ8/cuXPz8/P//ve/79+/v7m5OT4+vqWlhSCIiRMn3n///YMGDTpx4sTnn3/+ww8/BINBbcxvvfXWO+64IyMjQ6/XwyaYqqhOp/P5559PS0t76qmnGhoaXnvttUAg4PP5/H4/RVF6vX769OkPPPBAXFwcQkjTGs6cOfPQQw+Fh4d/8sknUVFRYJNUV1e///7727Zt27JlC0mSy5Yt2759O3yrvr6eYZiUlJTnnntuxIgRHbWGLVu2bNiwoaioyG63y7IMkQLBYNDtdhuNRqvVajKZHn300by8vI5ag8fjeemll4qKihobG41Go9/v/5//+R+GYXw+X1xc3IQJE1RVpSiqrq5u06ZN69atq66u1h6va2pqEhMTZ8yYMWfOnOTk5Haz6XK5Tp48+d5775WVlfl8voiIiJaWFoqiAoHA7NmzX3755SVLlnzzzTexsbEcx5WVlb3zzjtgz3i93qVLl4KJiBBSVbWtre2DDz7Yv39/fX099LG2tjYyMnLUqFG/+93vsjKztAEhSZLn+fXr1y9fvnzLli3h4eHLli1btmwZQkiv1xuNxuTkZO3IPQGO/MILL+zevTsvL++9995jWZaiKVmSN27c+O677+bk5Pzxj3/88ssvN27cWF9fHxUV5XQ6ZVk2GAwzZsy4/fbb09LSerhX6Xa7d+/eDeuZIAhtPRsMhueee+7OO++kKVpRlca6xoKCgq+//rqqqkqbi5aWFrPZ/Jvf/Oa+++4LLRQiimJNTc2GDRtWr17d1NQUHR3NcZzb7YYqD5MmTZo3b97QoUPMkiF5AAAgAElEQVS1+0BoGyIiIiB6pampKT8/Pz8/f8SIEeDZQSFKJTrfTAbTHZKerl+/fs2aNaHXkU6nu/nmm//rv/6rf//+6IKx+v3333/00UfFxcWwdfzDDz8cOnSora1NVdXZs2fn5ORA3E1PxhD23s+ePbto0aLExMSRI0euWbNmxYoVxcXFUVFRFEU5nU6dTjdt2rT8/PxBgwZpCSC0vhw4cGD9+vXffPMNeOtQFOVyuSIjI6dMmTJr1qzMzMyfbQOMZDAYrKurW7NmzYYNG7xeb3h4uKqqFRUVZrN56NChjz766PDhw7WTwl79zp07H3/88UGDBr3xxhsDBgwIPabZbFYU5euvv164cOH8+fP/+te/hoWFFRcXP//882fOnImIiFBV9bPPPgN3oZaWljvvvHPhwoXR0dGyLIuiuHLlyo0bN7a1tVEUBZd5bW1taPKL9PT0P/3pTzk5OfBiMBjcuHHjqlWrTpw4wXGcyWSSZdnv96elpU2cOHHq1KkpKSkQEQOa+KFDh5599lmdTvfll19Cp8AXYN26dcuXL8/Pz1+4cOHHH39cUFDg8XgYhgHb3mAw5Ofnw40ajqapV6H3NLgdtbW1eTwenuc1IfXxxx+//vrr4eLqodZAUdRPP/20aNGiQQMHvfHmGykpKeBv0tbW9thjj7W1tRUXF+/ZswfWv9FoBCe7urq6SZMm3XPPPZMmTdLWoSzJ4PUAvmOSLIVqDW+88cb69evj4+OfeuqpiRMnIoQCgcDZs2dXrFih/e54vV6I5VEUZf78+XfffXc7+QmDwWAwGAymGy5RaACr/sCBA//85z/3798fERGhpUjgOM7j8SxevLiqqmrhwoWX6VkATzk6na6goODNN98URXHYsGEjRowAc66urm7nzp2LFy9mWRYi/8GKgP38Xnkk0nYUX3zxxdWrV8fGxk6ZMmXgwIGyLFMUVVlZuXXr1vr6+tbWVp/P105oaGpq2rZtW319/ezZsxMSErTQ/e++++6jjz46duzYu+++C9uwAERYlJSULFmy5ODBgzNmzIiMjBRFsbW19cSJE+vXr5dledasWS+99JLBYJg/f77RaFRV1e/3//DDDwUFBWWnyj5b+Rm64FFMUiRFUclJyYIgrFq1qqysLDs7OzY2NiIiwmK21NbVfvHFFxs2bKitrf3Tn/6UkZEBYSkQLmG1WiFhATSMpmmDwdDS0kLT9Lfffrtr1y5oSVpqGsuxra2tlZWVBQUFCxcufOWVV6ZPnw4uA+Btu2vXrj/+8Y8sy86ZMyc3NzcxMbG2tvb06dMbNmyorq6OiYlZtGhRU1NTdna2wWDoOGUsyw4ePDgrK6uhoeGbb76JjIycNm0auGCgC374JEk2NTVt376d5/n8/PzExMTo6Gie548fP7558+YPP/zQ6/U+88wzHMdBYDYErWzetPnvb/9dluUhQ4YkJiZarVZZloPBYGVlJeQ76NOnz0MPPbRz5862trasrKz8/HyPxyNJUjAYHD58OM/ziqLo9fqSkpLXX3+9vLw8MTHxN7/5TXh4uNFodDqdhw8f/v7776uqqn7729/efPPN2loCh+ekpCSn07l48eLPP/983LhxMTExFouFJMnrrrsOofbB+T+LXq+3WCwmk0nzkSEpEnzCXS7X0qVLCwoKfvOb31gslvj4eFmWKyoqjh8/vnr1ar/f/8wzz2gqYadoQfWffPLJu+++azabR48ePWTIEG09l5SUjB07VlEURCFVVd1ud0FBgSRJN998c3JyclRUlCAIFRUVu3fvXrNmTWtr65tvvmkwGCBu3O/3v/POOzt27EhNTZ03b5520vr6+qqqqri4OFCIRFGEFq5cufKDDz7Q6/Vz584F56C2trbjx49v3779dNnpJ558AjxuILa/HYqiqIqqqApJkEVFRW+++ebBgwdzc3OHDh0aExODEGpoaDhz5sz69ev379//6quvZmdnawkjRo0aNW3atGXLlp07dy49Pf22225ra2ujaXrEiBGyJPdQZUAXcmpAIgm/3//3v//9q6++6tOnz5/+9CftM5s3by4oKDh8+PBHH33Uru7AgQMH/vKXv9TX1w8bNgz8DiIiIoqKimpra1esWHHo0KEXX3xx6NChPVk8oDwePnw4Pj7+5ptvtlqt0dHR9fX1hw8f/vHHH59//vm77777zjvvDAaDZrP5vHapqpD/EiKY2rk8gLO91WqNj4+HqDGz2Zyfn+9wOFavXs2ybG5u7rBhw8D1DDyY4C79xRdfvPXWW/Hx8fPnz8/OzgaJ8OTJk9u2bWtqaho7duzUqVNjYmKysrIQQiCSLl26dO3atQzD3HrrrZGRkTabjeM4UKxWrlxZX1//3HPPGQwGLUkE1I+MjY0NTeUD2kF4eHhFRcWrr7769ddf5+fnx8XFJSYm+ny+48ePFxYWfvzxx5IkvfTSSyCRsCxLkuSGDRv+8pe/WK3WOXPm9OnTp2/fvg6H4+TJk8uWLVNVNTY2dsGCBTabLTExEaK9en4tS5Kk0+lkWYa7N7qQc8Hn80Fc0ocfflhQUBAREbFw4UKWZW02W01NzYEDB0pLS1988UXNEQaOFuoSghCCfBYURS1evHj16tXDhw+fP39+Xl4eQkgQBI/H8+KLLzY2Nl533XVDhgwxGAwQkFVaWlpcXGyz2aKionrYCwwGg8FgMBh0yUIDBHauW7euqKgINoHB8AMgodSXX345fPhwcG6/ZCiSIgjC4XCsWLHC7XbfeOONDz30UHp6upY2b+zYsS+++GJLS4vFYpEkSZGV3k1VBZucO3fu3LZtm9FoBBcAjuMgutXhcOzcufP//b//B/uo7eyNhISEBx980OFw2O32pKQkzTAbMGBAZWVlc3Pzli1bHnzwQe3zLMvKsrx//3673f6Pf/wjPT3dYDBAjrGvvvqqvr6+oKDgyJEjkZGRTz/99LBhw+ABOhgMxsTEvPXWW2fPnS0sLBw7ZiyMADzderyesrKyurq622+/fdasWXa73WKxgL/9gAED3n333SNHjhQUFCQlJcG5LBYLRZ33uUUXIs9h7zQsLCw8PHzVqlXp6emPPPLI4MGDtcRjRUVFNTU1+/bt++6770aOHAnFIBiaqa2t3bJlC0Lotttuu++++yIiIoLBYFpa2vXXX5+QkLBw4UKO4xISEsA47FSTMpvN99xzD0VSy5Yvc7vdOTk5YAzAu7IkQzczMzOfeuopmqYzMzM1T/vJkycbDIY1a9bs3r173rx5UVFRAiEY9AZFVU6ePLn0w6UOh2P27Nm33357dnY2upABxOl0RkdH+/3+e++9F8ynoqIiq9V6xx13aDv/IHDwPN/c3Lx06dJjx44NHDhw3rx5kyZN0lo+evRoq9W6d+/e119/PS8vz263w+KELdzq6uo1a9YUFxfff//9c+fONRgMDMPAbip8/WK1hnYosgJ2lMvl2rJly+OPP37XXXexLKuN87vvvrty5crdu3fPnj0but8VYGquW7duyZIlFEVNnDhx3rx5fZL7aCkwa2prwsPDCYJQVIVhmIyMjCeffDI8PBx8JaAXPM9HRER8+umne/fuLS8vT09PB9f9AwcOFBcXg6U9YsQI7aSqqra0tCiKAo7rsBm7d+/eDz/8UKfTzZ8//4477oDMkaqqVlVVPffcc5WVlWvWrBkwYACk2e+qO+D/8tqrr50qOzVz5sy77747LS0NXNy9Xm9ZWdmKFSs2bdr0yiuvvP/++7CWJkyYACLF1q1bq6urx44de8cdd0DsOqyEi5oabVpbWlq++eYbcKDo06eP9oHMzMxnn322tbV11apVTzzxhPb62bNn33jjDZ/PN3ny5Pvuu09LjDpr1qwzZ86YTKZPPvnko48+Sk1NDQsL6yaoXhRFSZIWL178448/JiUl/fd///eUKVMg1J/juPLy8k8++WTz5s3Lly+fOHFieHg4usiyC62trXD2uLg4uL+9//77ZrP59ttvHzVqFLqgSuj1+kAg0Nzc/Nlnn+n1+vvvv3/GjBlgTg8ZMmTs2LFer3fnzp1hYWG33HILfAUirbZs2fL555/TNH3rrbfOmTPHZDIxDMPzvMPhSE1N/fTTTzdt2pSUlHT77bdDOE83MpB2jZSWlr788sszZ84MBoOgJsyaNevNN99cvXr19u3b582bl5KSwtAMqNs7duxACOXn5993331ms9ntdmdmZo4cMVKv1y9duhTi6fr3798uJK0n/GwBnXfeeWfq1KmPP/54bGwsHFwQhMmTJy9cuNDhcKxZs2bR84vgk6C1ESEghGiafvPNN1euXDl8+PA5c+bk5eVpwTjbtm2TJAnc35KSkgiC8Pv94KzR1NR0+YU2MRgMBoPB/KdxKfGW4KxeWlpaWFgI8d7tdk5omjabzc3NzQUFBZfTOLCHGYb5/vvvq6uro6Kipk2blpmZCQmxYSPx+rHXP/jggyaTSZIkcBBFl22khaLT6QKBwOeff97Y2JiZmfnAAw9AmD08hxkMhttvvx2e+WRZhiSUWv4tURTTUtOGDx8Ou7KKokCEdkZGxujRo/1+f3Nzc+i5IGGeXq+fN2/egAEDQLuBiN9Zs2bBQ7YgCPfcc8/w4cMhHt7hcOh0uttuva1fv35er/fYsWNenzf0mHq9nqKo1NTUu+66KyUlBYJZwDQaN27c5MmTXS7X4cOHjx8/7vF44F3mAqHHIUnSaDRSFOVwOJ5//vmhQ4fyPO9yuZxOJ0Kof//+N954Y1hYWFFRkcvlOl/QgaZkWd62bZvNZhsyZIhOpwNToaGhQZbkCeMncBzX0tJy+PDhQCDQleeLIiuQIC0sLAysx9DFRpCEKIqQlW3YsGHZ2dmh8fwsy/7ud78LBAJGo/HUqVMWi0Wn04mSyDDMypUrXS5XamrqokWL0tPTtXBulmWjo6Nho5VlWZ1OB1ZZc3OzJElQD8Lv91M0RRCETqfbu3fv/v37WZb97//+7zFjxkD4vcvlQggNGDBgwYIFsMO/fPnydmXqUlJSjh49OmvWrHnz5lmtVuh+u2SKWprGSwC6KctyY2PjhAkTQGXweDyqqjocDoTQLbfcMmDAgMbGxuPHj0ODu4JhGEEQ1q1bx3Hc4MGDH330UU1lQAhRNJWYmGg2myGvXmtrK0Jo5MiRAwcOhNIA2lzMnz/f5/OFh4cXFhZCBRloErQnISFBuQBcX+Fh4dHR0dpZEEJr167leX7QoEHz58/X/NgJgoiJibnzzjv1ev3WrVt//PHHrlQGkiShRE5hYWHpqdK0tLS5c+cOGjRIr9PD/USv02dlZT366KORkZGtra3vvvsuOOr7/X5RFP0BPyhB9fX1kiRBsRJYCVq+vYuaI5qm4+LiHnnkkZSUlFCDcNSoUQMGDAAv+tBj7ty589SpUxzHLViwoF/ffqHlV5KSku6+++4RI0bs3r37u+++g/iLbib0wIEDUAnid7/7HYTqQApDRVESExOfeOKJqKiotra2JUuWwAWlhRu0M1xD0W6Mqqr6fD50Idso5CUVRdHpdJ5PMUjR4PKg1+vPnDnj9/ujoqJGjx4duv6tVuvo0aMFQdixY0dzczNonQihQCCwZcuWqqqqsWPH3nbbbWFhYXDtMAwTExMza9asmTNnEgSxYsUKuLu2y00QCiQ7hNCbWbNmgasUFDCCIkd33XVXRkZGc3PzkSNHXC4X/Bh5PJ4ffvjBZrMNHjxYp9M1NjZyHNfc3CzJ0vjx45ubm6uqqhobG1FIMFovEh0dDSqedsNkWbZv377jxo1jWXbfvn2i1GX1pZaWljfffHPHjh12u33+/Pnjx4/XFonJZHI6nRAGFRYWBhsJUMrEZrP169evd3uBwWAwGAzmP4FLTOwEceYEQej1elVVQw1shBDHcZIkxcXFQXrIS0ZRFYSQx+NZv359IBCw2+1Tp06FvOKKqoiSSNO0rMgTJ0602+1QF0CzPS7hdKFd0JAlub6+vrKyUqfT5eXlmc1mg8EA/reQ8QshNHPmTLvd3tra2i57GcMwBElAkAVU0YMX9Xr9iOEjNMNVw2q1SpIkSdKsWbNomgZnXYQQ7CwlJydDhYJp06ZBhTNZkcEQCgQD4DN/viRESI/q6upUVR07dqzVaoV8/qIoKvL5HcUJEyZkZGScO3fO5XKZzWYopiBeQFVVmAKwo2RZdrvd48eP79evn8vlMplMFrPFaDTC83pGRgZCqKioCLYEoQE8z0OoMyhBUFATKiCAhQbmR2gwdjucLqeWnR4e3EVRDC35BkPRzqxSL1Sm1Ol0ERERLpfL4/ZAlnWdTtfc3Ozz+YLB4Pjx4+EVaEYgEPB4PAghWZa1fHImkwl8sKGnkEYOzuJyuQoLC81mc1JS0g033AB1BzmOgyoGgiDEx8dPmTIFIfTtt98yzPkCCizLGo1GURT79u172223GQwGnue1HvWWZQIVNBVFcTqd9913H8uyfr/foDewLAs2XlxsXHh4uF6vP3XqVPc5GnQ6XUVFRWlpKcdxkyZNioiIgNAkeFcz+MG9BRwQQveQQQnieZ4gCIvF4nQ6m5ub4et6vb5fv352u72ysnLt2rXSBXieDwQCsPbQhUISZ86cOXjwIMdx9957L3xXlmRBEEBwHDVqFDh7V1RUdNMXuELXrl2rqmpmZmZWVhZIBpIsSbJE0RRJkuHh4VOmTGlubi4pKVFVVZZkuNJhAUuSZDKZwIQGcxohBBkiezh3Wn0Km82Wm5vbMYOjTqcbOHCgxWJpamrSjo8QqqiocLlcN9xwQ2xsrD/g93q9gUAA6h1CgtLp06fLsrx161adTtf93vjmzZtjYmJsNtuNN9543kWfpuAGBfra1KlTEUJ79uwB5agngMRAkqTVaoUBAVUO/qZpGt7lOI6iKYfDAcWA6+rqoAHgOoEQ8vv9ra2tIOMaDAaapkEygFVaV1d35syZ2NjYMWPGREdFa+Mjy7LT6SRJ8vrrr09MTKypqTl79qzH4wE9KBTtK2BIwzUyYcIEg8EAUi/oILIkR0dHgzoJMiV8y+fzEQSh3dOgyAvEboBZLsuyz+fTnK16l8GDB4eHh8NgapF6HMclJSXB3/+qe6qoUO1F++6HH364adOm1tbWhx9+WPNlAERRjIyMJAiioqLiu+++A71Pexdmqud1VTAYDAaDwWDQJQgNYIvq9Xqn0wmu5vCK9lwCHtFg0Ha/U/qzwKaN0WisqKgwmUxZWVnBYFCSJbA5wZhhWTbCHgGhtl0dR5Zkt9uNLpRJCwUMwnYvhj6VyooM+4oMw4AHb6ghDSQnJ0NsbccnS1AWVEUNBoMej6e2tvbUqVO1tbUlpSWQ5UtVVO2/pqYmmqbBqQHC5iHtvE6ng+hokiRTU1PhAZEkzpsECCGTyRQeHs5xXGNjY6gRjhCKiIiAvT44LJRn15KTh4WFwaiWlpaCQdXVGCqKAtUi4YkWqqxBYnOIQ6ZpGrLZhW60QmFOSDtns9nAGCMIArb3wZAITQ3YkbCwMHiehiyMUHhS206HAW9qagL7QZvKQCBQWVlZXV195MgRgiCampoEUdCOc/bs2TNnzrS2tmppF0Ed4DjOaDDK0vmyBTB9Xq8XDBKthpxmRVut1pKSktbW1rS0NKhQqE0ZwzAQhz9ixAie50mSrKys1IYFEmGkpaXFx8cjhECQCn24b2cRXYJfg8lkgqmxWCyQgICmae0siqIQJAGzo6U/6IrGxsa6ujq4omfMmEEQhCzJmtcAmJewlU3TtCIr7a6yYDBYVVVVXl5+4MABSCYHCwYqNWZnZ+fl5RmNxo8//vjee+/dt28fTdNtbW1w4cARSJJsbGo8deoUVGb1er0VFRWNjY3lFeXl5eXlFeVnz551Op1er9fpdNbU1HTTF4ZhvF5vXV1deHg4RDOBiAYuPIqiUCQFJQ8kSWppaTl16hS4oAuCADqgoiiQzRRd2LGHQehGLOsKQRDS0tIQQs3NzeBSAf9Bl91utyZcqqp65MiR8vJyiqJGjx7NsqzZbIYrmuM4QRBEUQRNTZKkhoaGYDDo8/k6WteAKIqgqSUkJHT0XYJRGjZsGEVRoig2NDRor8PFSxJkxxudqqiaRwMQ+jfMOMQHQWP0ej2MGNwW4BIDg9ZgMNjtdoqimpqaFEVhWTY0w+XRo0dLS0uzsrL69+8feh9gGMZsMsOkgNfbwYMHux9/KOVIEAT8uMCwQ/OgEiRN0XCxyLKsyOednqxWazAYbGxs9Hq9er0eisjCt1iWhRug0WgUJbGr8b8chg8fDuMGXdZeB2eQmpoarfIuQJKkwWCIjY1977331q9f39bW9uqrr86cOZOhGVhs0EGSIG+66aahQ4f6fL533nnnD3/4w9mzZ+F2B7cvg8FAUmQvdgSDwWAwGMw1z0XnaBBFEcxRMBo1V2d0YdsNjCWO44LB4OXXmNSebAwGA1RA6HhMRVVMJhM4YHcK7PyLonjs2LElS5Zor+v1elmWr7vuupkzZ4Zu74Q+RocWsIiJiREEQata3xHti/AHBCofPXr0k08+OXXqFEIIdBmO43w+H2Rk0LZtEUJQMbSd//wlEGqjXsLXNfMgdGYhCaIsy7CD10MMBkNeXt6OHTuOHj06Z84cMGg5jnO5XBs3bnS5XH379o2Pj/d4PJfWaxhnyFLW3Ny8a9eunTt31tTUtLW1EQQBQRNerxc267SvNDQ0+P1+k8nUTeBxqPndLsenhtfrhWqd8fHxkiyB3QLB/FoywtjYWIPB4Pf76+vroRIqHO18jj1FkSU5NHNhLz7KK7JCURTHcee3rElKs6OAUCNQ7TraKDo6GhKU/GwAOUEQgiSoqlpTU3Ps2LFdu3bBXCCEZFmmadrlcoEMoZ2L5/mnn36a5/mdO3dWV1c/++yzUVFRc/5rzpixY6KiorQLLS4ubuPGjVAS9dlnn1VV1W63BwIBrfwNiHEGg+HEiRPdNxJ840VRtNlsbrcbUhJq71I05ff7wUMKXmkXiNHpBF3a3jVJkiBV/GwuSbB7e0JMTMzZs2cRQt1cp5IkgXKUnp7eaRAEQiguLs5oNPp8vtraWq3AxAXZgOg0OAXkuXb3wK7QdtqhEDLDMJ999tkDDzyAEAJPLoqitm3bFgwG+/fvDzJEj/rfgVBrH25o/5ISSATuZuDIgHom6hkMhtGjR+/du/fcuXMjRowAs59lWZfL9dVXX1VXV2dnZ8fHx4e6EvQi4DFEURTEFXb6GbiWFVUBxy6CINavX799+3afzzdgwIDc3Fyv16vXndfFVEWlaEol1MjIyFdeeeWFF14oKio6ffr0Qw89FBcX9/vf/z4rKwt8oBBC4FJ3JfqFwWAwGAzm2uOihQZ4zlBVNScnp7S0FBzs4S14vgG7BUya7pPMXRQ8z1ssFnCeD30WlCVZlmWr1VpaWkqSpPZ4F/oxaHMwGPT7/ceOHdO+C77lEBzekzZ0rzK0Q1XV48eP/+///u++fftiYmIoioqwRyQmJhoMBqPRWFtbu337drDBNGBPT6/Xd1My41/P8Z3l1b9ygBe0oijgy9ATSIK0WCwzZ848dOjQrl27nnjiifvvv79fv35+v3/Tpk0ff/xxTEzMzTffnJSU1NHT5KJQFOWLL7748MMPHQ4HqGB2uz0+Pt5ms4WFha1du9ZkMrEsC7YKQRCSJEEoRM/70imBQAAqMuj1ek1lkGU51MfYaDSCN4TmZg8f83q9YWFh4DJDk5eYk7UbeJ6XZImmabhkeJ6H7dnQz4RaX12tN7BnINtrR9+fjjA0s2fvnqeffhpSfqiqGh4eHh8fHxkZabFYvv76644Wo06ne/nll/fu3btu3brvvvuurq5u0QuLIiIi5s+ff9ttt9ntdkhgCeUGaJpOTUltbmk2m80QXgSXpMvlgtAMzSjqCEwWz/OwSduDUbyy0DQNUQZGg7Hd5Xyx1Qo0oFhM9x0E335JkroZK4hZAN8HdEEX6CZBA0IIdry1AIp2N+qOQBabjPSM22677csvv/z0009FUZwyZUpERER1dfW2bdsOHDgQFhb2hz/84QptpMNvB8S1IU2S6FrTgcVmsVhuv/32H3/88e23325ubp4+fXpCQkJNTU1hYeEHH3yQkJAwbdq02NhYzXejdzGbzeAw9f/ZO+/wKKr1j58pO7O72ZJksykbUkhIKKGFkhAIgtJUqpSr1wIqgvdyuRbA6xW9NtTLtf3siAVFRMWCNBGUIiJVSiAB0nvPpm2b3am/P14Y1w0JAYL1fB4fnyWZPXPmnDOTeb/nLQGea6gdZYcgiKamplWrVrEsK0lSdnb2hx9+6J+BGIBb22KxLF++/PTp02vXrj127FhJScm9997bo0ePBQsWpKamQtLNLr8iDAaDwWAwf1Qu2sKBpAwEQUycOBHqorc9xmg0trS0MAwzadKkrugkQghJkmS329u+S0mypCgKbHV29HVR0tCaxMTEW265Rf2hw+EQBKF///4Gg6EzWoO/ytB2QwlkC3jJRgjJkvz888/n5OQMHjx4wYIFI0eOBPEFsqCvWLHi0KFDAeHZ4E/OMIwiKwTVkdDQgWV43m536rBzyfPbc/eFoOsOwiv8URRFVmSGYUaMGPHOO+88/vjj33333b59+xwOhyiKwcHBNptt6tSpd955J7w3X+xF+fP9998///zzFEWNGTPmb3/7m81m8+/k+vXrW1paIEYGyqBC5k6n02m32yESpL2dzI5XBbx5cxwHYdtQcz7gElwulyAIalQ/AJkIwKMh4LyQ6DTA7LyEYYHqmwghtRihrMik/LOVIEmS6hnuXzXmZ/2hSIRQUFBQe24dKnAhJ7NP3nvvvRzHjRkzZuHChTabLTg4GEozEASRlZWlhpAAYBizLDssfdjQoUPdbvebb7757bffchy3Zs2awsLC++67zxpmVRQFQuIZhvli/Rdg8nEcB4Yio2F8vA/cNzqYMijKC+4MkPowd0IAACAASURBVC3CZDKBBKMOOAQQNTY2BuStINrQ8Wh0BvBogFok7XX7bNKZ9lVFRVEoklIdoxwOR2RkZID/fABnMxHQtL/QCbeA+qwAJUKj0XTyfg/oEnwAnxpYOaqNCrviYNKTJEnR1JIlS+Li4v73v/+99dZbn3zyicfjgVs1ODj4scceS0lJuZxddIIgZEk+fwiJKMANC+4w0CUYyfPmIwAnCCjv+vjjj7/00kuff/75e++9B7UnExMTIePpjTfe2BlJ7tJwOBzQjU7WOoHRDg0NfeGFFz788MMjR46sXLlyyJAhaUPPVniRFRmJCKKoEEJGozE9PT09Pb2wsPCll17KysoqLCycNWvW888/P23atMv3tsNgMBgMBvPn4dK3UhMSEhYsWPDGG284nc7w8PDGxkawVURRdDqdkiQNGjQIanRfMmDMEwRhs9nKysqqq6sJglDLtqlhsQih/Px8nU4HTtGQFlE1BtT3S4IkoqKi7r333rYn6sA4kWQpKioKdggLCgr69OlDEERzc3NQUBCFzgoN8A4N6eLgJ4IgfPLJJ9u2bRsxYsTChQszMzPBkx8sHJ/PV1tbC2/bAd0ICwvr2OdWvahOygeKotA0zfsCX3whggMhVF9f73K5wBwNOIaiKIgcAVcRqCzQsQ3j308YVZPJZLVahwwZcuDAgf79+zc0NPTq1WvYsGE9e/aEDAX+7rhnZ8qvJ4r8k6vzea2F1tbWd9991+v1Tpw48Z///GdUVBQ6l4wD5oXneSh9KkmSJEqSLCUnJ1utVofDUVtbGxMTA6cDkwPWsKIoNKLRuUSGqnd0wDXqdLqgoKCamprm5mYI3acoiiRIVYHieb6srAymQC3JSRAEFChxOp1qfVB/ZEX2ur3wK4jEViddbSHgK8rPgWFHCEmSpGZqVBSFoC7aQgbLPDg4GLzZS0tLBw4cyHm5oKAgtSnVQpNl+dVXX5UkKT09ffHixSDiwFy4nW6TyVRbWytJknIuHsf/Fm5qbtJqtTRNL1y4cOHChStWrNiyZcvmzZvHjBkzevRoiqB69OhBkqQgCNnZ2f369QMnC3Bq8PE+vV6vptVo71ogf2S3bt1gRiCbA8uy/pvDJEWC6z6kPEhISICfg2MIfY4ud4wPmBc1quVsr0gyIiICnjDl5eXg9wS/UhSFoikkIlmW7XY7z/PRtuiOYzFALIbyGQghcBjxl/kURSkuLoYlDRVz4Fcsy7a0tMiSDOpDQMhJS0tLQUGBupNPnCt3ynEcTJbH41G1VLg3KYJCCHk8nqSkpISEBKfTmZqaqtPpwsLCIiIirr/+eqgh6t95q9VKUVRFRYXD4XA4HEajUe22emRTU1NQUFBCQkLbexbuKYQQSZGUQinnshera7iDlIeQ4gTC98LDw202W21t7fDhw0VRTEhIGDhwYEJCQmxs7CXUH+k8Wq0WVCdRFP3FQdWXUP3DB6k04C/FsmXLUlJSli1bNmvWrKampqVLl77++uuQlkIWZFmRSSUwJtFms7322mvNzc3PPPPM3r17X3755auuugpS516hS8NgMBgMBvMH41KEBvXF7i9/+UtZWdmhQ4fsdrvqWc0wTLdu3Ww22wMPPACW5CUjSiJshEJe+sbGRlmWIaG6v2suJDYDe4w4V4YtsM+XGmgA9d5EUWxtbc3JyenTp48gCOByDDv8DMOcPHlSURSKouBtz+fz2e32kpISq9Xav3//Xr16QX/U/GeSJOVk54Ai43+unyzJ9nsLbhGwqdUZwGSqrqkO+DlcQlVVVUVFhVpSQa3ZAVAUpb67Q2ZBqJ3RqRMjhBCSZdnhcCxfvjwvL2/58uXDhg2TxLNpKfwrdKh7+P4WdedN4oKCApPJ1K9fv7ZRGKdPn4YN8582LBUlODgYJu6HH35IS0tTz6W+ahMEAcMLa4/jOHXjPcCEGDx4cF1d3bFjx5qbm00mE7g0Nzc3BwcHEwTBMMyePXsURbFYLGCwoXMSDBgJ/sE+/gTsHML4XKxG4O+7DrdnwNyplklHxhVB8DyvBvMXFRW13WEmSRKRiJAJQRDy8/ONRmPfvn0TExP958JkMuXn57vdbnA6UM7VMYFSHVqtNjQkVJIlNSXKHXfc0dDQsH379pqaGuhez549IYnJjh07+vXrB6qBrMhQKwGKFECCyQ7MbFjPmZmZe/bsKS8vh8SuBEmoaTUIgmhsbPzuu+/QuVoSDMOAbQxXCvinbrnSwI3JsmyvXr2OHj36ww8/jB49OiYmBn6ryIqMZIqmHA5HVVUVz/MpfVM6Fho8Hs+oUaM++uij4uJiu90eFham+mdBvQ+CIA4dOgRhAv5VbMxms81mq6mtcTgckRGR/m1KsuR2u+vr60NDQ2HVkQQpSiIsQqPRKEmSRqNRO+a/mOvq6v7zn/+YTKY1a9aEhYX9rFlR8vl8kvRTntrw8PDExMSysjK73Z6amsrzvKrEwVU0NDTU19e73e6oqChIFaQmfST8SsaAhAcPbYqiLqgywFDDIoHCn4WFhWvXro2KigLhhuM4+AuoJhK6YPDIpUFcTDlV/3GWJOmNN96YN29edXX1iy+++Prrr4MuD1k/oUFIYYsQggE3m80LFiwoLy/ft2/fmTNn4uLiLtnvDIPBYDAYzJ+NywpUhsRsUL89PT29d+/evXv3Tk9PHz169LJly9TNwMsB3mmuv/56hmHy8vK+/vprhBBJni0KQBBEVVXVpk2bYOf2ShgAJElGRkampaUZjca1a9dWVFTAexiYnbC9s3v3bthfCvA/V+Oc/S0uj8ezd+/eyqrKsxuwBKn+15n+qG7wney/Xq/3er07duzYu3cvlN4AFEUpKyv79ttvnU7noEGD+vXrp/5c8ufcZq9aetBfIOgASZQgN0F1dfWmTZt69+5tNBoFQfDxPvBgh6HgeZ4iKUmWOtlsWzoQPhobG7/88kuNRgM2Nlj1MKHp6emyLH/xxRfZ2dk8z0PUgzoC6iXQNA3h1h6PJz8/P6B9giAyMjIIgqisrNy2bRvUWKFoCgrRI4Ty8/PPnDnDsuzo0aPVxsGjob0+w1Zzc3PzqVOnjh07BlKUrLSbs6PzXFoLBEHotLq4uLgRI0YEBQV9+OGHDQ0N/hYONAsD2MFc1NXVffTRR5Ik+UcfwKYrBD1RNMUwDNQ9pWjKbDZHRERAbgtY7ZGRkVdddZVWq928efOxY8dgJx9cSBBCLMMihBwOxwUTK8qyPGPGDEVRioqKduzY4XK5SJJUbyie50+fPp2dnc2y7DXXXAN2KcSYwAe4BUTpAoEkF4v/sCC/DWqO42BlBgUFdY/vHhERceTIkYqKipaWFtWhANSBAwcO5OXlBQcHjx07toPkCwghvV4/ePBgrVZrt9vXr1/f3NzMCzwocSzLQu2Gbdu2eTyeyZMnR0REoHOzHBcXFxwczHFcXV0dQRKyIqv/8QKflZVVUlICywNmV1EUSZREUQwNDXW5XLm5uVCMM6A/hw8fzs3NNZvNauUgteArRVMsy/qHb1it1jFjxrhcrs2bN1dXV0uSpOYJomgK/hwcOXIkdWBq//79kd+D66xPk18hD1i08MMLqgwIIUEUYIXX1NR8/fXXmZmZatJHmCOSJFtbW0EYgjI0F574XxBGw0RHR99zzz0xMTHHjh2DcDOaphX5bN1fSA+kPvNhfGw2G6wBDAaDwWAwmIvisrLQmc1mWZZnzZp1ww03NDU1lZeXI4RiY2MjIyMVRWltbfXfDbuUzlE0QojjuLFjx77//vsnT558//33o6KievXqBZ7SJElC5iqdTud0OsECVyMaugTYyZ8yZUpFeUVxSfHq1atvvfXW7t27g9BQVFS0ZcuWkydPonO2AUKIZdnw8HA4Ji8vDzy9wXO+trY2Pz//888/B0/sgBwNneSifApA+2hpadmwYYMoioMHDwanervdvmvXrm+++Uar1Y4dO3bgwIHoXHo2nufBT57neV7gdbQO6ucBsGmstg+B3G1fqQVR0Gq1kiQ1NjZGREQUFxd/++23jY2NPM9DcUeoOR8VFaXRaTycB1zx/T0a4P8XtLEpikpKSjp69Gh2dnZ5eTmETgBfffXVrl279Hq9w+GAynOq4/TUqVP37t3b1NT05ptv3nXXXbGxsSaTyW63g41UU1PTr18/SZY0lMZisVgsloaGhsOHD6tyDEJIlmSSIq8effXYsWO/++67zz//3Gaz9ezZ02azSaLk4Tzff//9p59+6nK5zGbz7Nmzf/riz+N0VNXs7LgJAmhqb7/9dmFhISRE7NhduZP2zCVLFRRN+Ty+GTNmZGdncxz3xhtvzJ07Nz4+Xj17Tk6OwWCIjIxkWTY5OfnHH3/MyckpKiqC0Alg+/bt27dvZxhGrQ4LuuCJEydqa2vBB8r/MrOysg4ePCjLcnh4uOq4dNNNN0HLL7/88u233x4fHx8SEhIcHEzIRG1tbUtLy6lTp6ZMmdKx1qDIyogRIwYNGnTy5MlPPvnEYDCMzBwJe9oESZw5c2bVqlWKoiQmJs6ZPQci4bVaLcTFwI0A/4eruNK7uz6fD8xslmFHXjWyqLjoq6++Wr169ezZs5OSkiCaCZ4z27dvLysrGzNmDLh+dDQCitK7d+/Jkydv2bIFanlkZGRATlytVpubl7thwwZFUaKiom666Sb/L5IkGR8f73Q6t23bFh4enpSUpP7qwIED69evB+cF/6+A3GA0Gmtqan7Y+8OkSZPi4uICPGIkSYqKiiooKPjss89CQkJAONDr9VqtNjQ01GazmUwmiO+Azg8YMCA5Ofno0aMbN24cM2YMOHdoNJpTp04dPnx4z549ZrN5+ozp4eHhkijptDpe4NWJUx9fsiQL4tmf8DzfGZ0X1ARFURobGxMSEnJyctatWxcfH+8ftdSjRw9IDStKIkN1QYGG9pzXOpjis2qmHKhmUjQlCMJVV111+vTpdevW7dmzZ/DgwZmZmQaDobW1tby8vKGhYcCAAfBXiaIp0E1OnDiRm5sbGRnZu3dvNW4Rg8FgMBgM5oJcbrp7eN1hGCYiIsJ/34MgiMtUGdC5RHQQbrBo0aIXX3yxqKjo0Ucf7dWrF7xcQnLyMWPGkCS5detW2J6iado/hQHRYem+CwJfHzRo0O133L58+fJ33nknNze3b9++YWFh9fX1BQUFZWVl06dP3717d2lpqXpSn883bty4zz//vKGh4dVXX42Pj+/ZsydN06WlpceOHTObzXffffejjz7KcZx/lARBEF6vF6oA+hdEAGMeLHN/88bfwhQE4WyOAOqnKveQMC8kJGTgwIHHjx8vLi5OS0szm80ulysvLy8rK0sQhOnTp99x+x2Qrk+RFZZlWZaF7ANQ+A38aU0mU1BQUFBQUGTkz12mpbOeC1D/kqKopqYmW5RNq9W6XC6DwTBw4MDZs2evWrXqrbfeio6OhrxrCCFZlq1Wq16vnzBhwsSJExVZIemfXp3VKVMDOtwed1BQkCRJFovF/8Ipipo3b15JSclXX33l8Xh69+5NkmRzc3NVVdWxY8dmzZp1/Pjx0tJStRgENNu7d+/77rvvvffe27t3b21NbWxcrNVqhWz8WVlZY8eO7du3L0JIluThw4eHhYWdOXPmk08+gWqI9fX111xzzYABAzRIQ1LknDlzgoODV61atWzZspEjR0ZFRYmiWF9fn5eXV1BQMGzYsLlz56qKEuzeu91un88XFBQEJrH/4lSFodzc3Pr6+oiICIjKaW99ejwerVYLO5Ow8Q5btSRJarVaj8fDMAxIS21NKVB8wDWgY7VCr9ePHj169uzZK1eu3L17d1lZmdVqTUxMRAgVFRXl5+dPmzZtzpw5jY2Ns2bNKi0tPXTo0JIlS4YOHarVat1ud0NDw5EjR66//npBENavXw8e5qArHTt27IMPPoiLi4uLiwsLCwsKCuI4rrq6uqCgoKio6K9//Wt6ejrcgzRNJyYmTp48eevWrdnZ2cuXL+/Xrx9cmsPhoCiqurr6+PHj1113XcdCA0mROlq3dOlSSLD/5JNPjhw5Un2enDx5sqmpqWfPnvfccw9EE8BSBPtKq9Xq9Xp/xepigaeZmiHlvCiKYjaboQJrcHAw6FAUTUVGRI4bN85ut+/Zs6empiY+Pt5ms2k0mqqqqtzcXK/X26tXr9tuu81qtXbcB6g3MWPGDITQ+++/v2rVqpycnODgYIqiamtrCwsLy8vL+/Tpc9ttt8HNrq6N6OjomTNn5uXlbdmypbq6uk+fPhERERzHFRUVFRQU9O7de+rUqWvWrPnZaJOk1Wr9xz/+sXDhQrvB/tJLL2VkZMDzbcqUKUaj0ev1jho1atu2badPn3722WdDQ0MhEgGkHK1WO3z48PT09IyMDISQLMuhoaFjx471eDyvvvrq8uXLd+/e3a9fv9DQUIhWKykp8fl8M2bMyMzMRAhBJU5aoQ0Gg1artVgsWq32rM8ISWi1WhBDO3AAURTF5XL5fD5BFNQSmBkZGePHj9+4cePzzz+v0+ngJoJbLyEhISYmZuzYscOHDwctsm2bgihQNGU0GkGUYVkW5GyEEKQpgQWvnEst5HQ64WnscDjU/gd4z4EWo2ookF4UKrN4vV6WZaF9+DuyaNGi1tbWtWvXrlixokePHiCObN68efv27cOGDTMYDKGhoUajsampyW6379u3z+VyzZ07V6/Xa2hcdQKDwWAwGExn6bK6eldiZ8+/zYyMjCeffPLjjz/eunVraWmpRqMxGo1ms/mmm26aN29eVlbWDz/8oNFoNPRPmRq6ELPZPHLkSKvV+vzzz5eWlp48eRISN1qt1ptvvvmWW26prq6uq6tTEyWCCTdnzpyvvvoqKysLor4FQUhMTBw+fPiNN94I5l/A5ja86xMEoWZ6A6B6n8fjcTgc4FQsyVJALkmv1wsyhP+1Q/ixxWKZOHHiLbfc8tprr23YsEGSJFABYmNjp0yZolYGkaWznYGYZ5IkwamYpmlRFBsbGxFCDofD5XSd1yi1WCxgQLIsy3k5SZIMQQav1/vZZ5/l5ubGxMTA5mRTUxPEuciynJ2drdFozpw543K5br31VrUpf48GyEmBEEpOTgb7vKWlBVxp4GCSJNPS0v72t799+umnBw8ehOh6KG85d+7cadOmhYWFrVy5ktEwar6J1tZWQ5Bh3LhxRqPx008/LSkp+eabbyBmm+d5o9FosVggNyR41D/66KOvvPLKrl271qxZ43a7NRpNTExM//79BVHQIE1CQsLUqVMjIiI2btz45ZdfiqIIqRxCQ0OnTZs2ceLEvn37QmFFMP7BjIyMjGxqanI6nSzL+juTQxqCuro6r9cbHR2dmJgItnR7QgCEhEDAxU/7qrKCSERRVHh4uBoS0jbxB8/zXq9Xo9FUV1f7e4K0x5133tmtW7d169bl5OQUFBTAUEN1gISEBI1GYzabhw8f/sADD3z22WfHjx+vqKjw+XwGgyE8PHzevHljx449efLkli1bYHnDVffp06dHjx75+fk//vgj2EUg90RERMybN2/KlCkWi4UkSFk669k+Z86cvn37btq06fjx4/v374dwFbPZzHFcWFjYjBkzAvIatgV+FR0dvWTJkm3btkFZTTW/TGJi4tSpUydNmqRu1/s31draKghC2yCpzuN0Oo1GI9iWat6QACcsuPtCQ0NhiH5KCUmRAwYMMBqNCQkJ33///YkTJ/bt28eyrMvlioqKmjBhwvjx40Fo67gPoAxardYZM2bYomwbNm7Ytm0bODHpdDqNRjNmzJgbb7wxdWCqJEtOp9Pf8Wrs2LENDQ0ff/zx8ePH9+3bRxCEXq83Go1Tpky5/fbbT58+/fnnn7c945AhQxYtWrRmzZr9+/fv2bNHkqSkpKSxY8cajUZRFL/66iufz2c0GiFPpyo0yLJcUVGxatWqY8eOURQFxZIheWdGRgbDMN98882BAwfy8/NBig0KCho4cODkyZMzMjLOimvnrGvQFKBArzrICCG9Xg83ewdjBa468HWdTtfU1LRhw4bTp09D9VyLxXI2b46iKIpy9OjRoqKiM2fOeDye8ePHnze1ilarhZI3brdblmUofOuf4FNzDrgEo9Ho8XhEUYR6sWr//Z8Jra2tDMNERkaqzx+QeGC5BgUFBfTh9ttvb2ho+P7771944YV//etf3bt3Hz16dFlZ2c6dOyFiQpZls9nc3Nzcs2fPW2+99aqrrgoODuYFnkFMQHpODAaDwWAwmPNyRbJVXQl4noeIZbvdXlhYyHGc0WiMiYkxm83h4eGtra2VlZU6nU7N7e/v4dlV10gQRFlZmdvtzs/PdzgcsbGxycnJEeERFE2B32nv3r31er2iKPCi5vF4OI6rrKwsLS0lCMJms4WHhzMMExcXV1VV1draarFYwsPD1farq6sbGhooiurZs6e/8QnZ8srKyjiOi4+PV7eV/BMoNjQ0uFwuURSTk5PVL+bm5s6ZM0eW5Wefffbqq6+uqKjQarWHDx92OpzdE7prNJrY2NiwsLAA132aoo8cPWI0GiEtuTr+cI29evVSvS1kSYY9Q8iFlpOTw7JsbGysukP4xhtvfPTRRzExMfPnz09JSYFXalmW4S25sbHxs88++/bbb6+++urly5dDmkBBEIhz1R8QQpBck2VZh8ORk5NjMpnO+hrIP4VUKIri8XjKy8vz8/MhCDw6OjohIcFgMOh0uubm5srKStVuhKnRaDTwWl9RUcHzPLg/xMTEWCwWg8EQERGhOm4oiuL1ep1OZ01NTU1NDU3TFosFvNNBO9DQGpIiXS5XU1NTVVVVfX09ZHaIjo6maTosLEz1+kYIQYGPxsZG2JHW6/UBCSYJgnA4HC+++OLHH3984403PvboY+q+uv8x6mdFUXied7lc5eXlSUlJqk3o8Xg8Hk9DQ4MkSTBibSkqKqqurk5OTga/ic7Icw6Ho7q6mqZpWKsIIavVyrJsWloa1G2FapEN9oajR49CgYmwsLCYmBio7gEJC8EVAmAYpry8vKKiorm5GRKa0DSt1+u7desWGxsLu7v+VSGgtoXT6Wxubrbb7arQEBIS4vF4oGWTyXTBa4FEkl6v126319bW2u12hFBYWFifPn3AFUt1pQFAICgsLNRqtfCQCVD0Ljh0ACQv9Hq9hYWFkiRByFLbY2pqahoaGkJCQvzHSu0S2Kjl5eVQnzIyMtJoNEIl0U52Qy3L4vF4GhsbYd0ihCwWS0REBATCwJFer1fNbojO2bcNDQ1VVVXV1dU+ny80NDQuLo6m6ZiYGNBbe/furZ4IkmjwAt/a2trc3FxaWlpbWxsZGdm3b9/g4GBRFJ988skDBw4MHjx4/vz5DMMEBQWpQoMoilVVVU888URJScnf/va3RYsWqV2CxzskfSgvL3e73AajISEhwWQyRUVGndcSPnXqlNvtHjxosPoTSZaKi4tBeO3evXvbryiyUldfB0Z+r1694NT/93//99VXX/Xv33/27NkpKSlqkgggOzv7zRVvlleUjxo1asmSJdYwKzg1BKwQuITKykpQBMDVQhIlzsspilJVVRUWFqYGoYB8nJOTo9PpunXrhtSiJGeTnBAIodbWVrvdrvbz7AWKUmlZqdPpbLvM4Fd1dXVGozElJQWSSpRXlFdXV3s8Hrfb7eW8ljALy7KJiYlqUmdIqYOFBgwGg8FgMJ3h9yE0wHsnRVKCKGhoDUGeTacHdgjYAGpm+PN+/fL74G/WCoIgSZKaSUsQBdiPAglAffkDm5am6PbezGCjW22Z4zg17ZnaZ0VRwK0AtXlbDTA+oXoc4VcV7/Tp04888khlZeUTTzwxdepUhBAk56+vr/cXOPyFBqfTCTHGAeG4kIse3ob9/YEDugS2kCzLLpfL6XTOnDnTZDLdc88948aNo0gKRgbkCUVWfLyvsrLy6quvHjFixOLFi9PT0yVRcjgdJqNJ3X5XCwr4D76iKDRFq92APoDEoMgKjDZs1yuKYggy+IeTAJIo8QJPEARIG7AlrtPpHA4HbOr+lBFNkSVJUsvstbS0BAcHgxWk+lyov/UfNFBSQMeBMVHnBc7V2toKO6UB82i32//zn/9s3br1nXfeGTduHPq5qtJ2zP1reaqotwOEe6B28E/af0HsdntoaKgoitCyfzlJ2JhVHb/Bx7u5udlqtcIBkii1tLYwDGM0Gv2FA7fHrXYP3NT1Oj1FU7W1tSB/BJTxgwqXauFS6Al8gKIDHMep7uUdAIoG+KeAwqVeC9T4CPAL8Hq9UIEiwNvoooDVC37yUHSjbWZEwOfzybLc3rng/lJdXfyfG51BHcCAkYQLbPuQ8Reh4AOsc47joBwGOrcIRVEM6DPcXyBVwC3AcRzUzaVo6sMPP3zhhReioqJWr14dHBysHoPOPUkEQXjssce++eabHj16fPLJJ+hcFl5oHJ78ASPp9XpB81IPg5wpEKbh/0yDvrEsC4lj2g6UugYkURIlURCEkpKSGTNm9Ovb77777xsyZIgaU6OOFUEQO3bs+Ne//pWamjp9+vTx48fDEz5gVOGZBuOvPh+gM+icHHm2DwTZ9vHlf0Z0Lt8E5ELmvJy/B4rD4QCXtLYPAbj3nU6nVqttKx9Af2iKBmnPv6gTBoPBYDAYTGfostCJKw1YFOrLkL8ZADs/7akMqKvDOgiC+Nm5SETRVNvXTdTG9mtLgJu3v7Xg32f/dvxdZwNaa2ts6PV61Q3b/xh/lSHgjOr7aEDSL9V+8I/nb9sHmBcojLdjx46mpqbo6Ojhw4czDCMIAoUoMEgYmkEk0tN6yAoZHh4OZe2gZIMsy2olOXCl9u8kDH5b8ejsq/m5dcEwTAdLgqIpHf0ziwjOol6+qnRQiPIff9g0Vh2bA/AfNIIg4KLUMQk4l5rEJGAY6+vrT506lZGR0aNHD7hSRVYIqt01fN6eqNfegcqAOrxr2gKXo37F/4rAwVv90kFFuAAAIABJREFUJ5zUP2kLRVMWi0X9HHAkQBCE2gjsqMN0qyVmEEKQrCGg8/4Ph04KATBoqknmfy3nlQXB974zLXeM/+ipJ23rTnKBNBMk+dNCJYiLUhmQ34JRP0CvznvS85qXMBT+Qw1NtV2KBEGAJqhmZoGzSJLkcXpycnIQQikpKWazGcIf1DHxer2SJDEMA0lPKIoCmcC/k7Ba/EcSnW+mICECavNMU1WSgDC0tkAlFEVRICYiyhY1PGM4OrdUQIuBtB0Qe+V2uymKGjp06FmXg3ae1f6hMWpnkN+yvCDQMkmRWlp73u92cPvDpLd3gNqfrk2ujMFgMBgM5s/DZZW3/MX4fW2kXFRvL3hwx3tZv2UgdkCj0UAINAQagNHI8zzU2ty6dSvs7KlSCKgMF2z8Si8J4hwXVePj8pFEKT8/v7m5ecSIERCBoijKeVPK/XnwN9V+19uq7fX893tFnaG91WswGAiCEAShpaVFTXvBcRz8FlI/0BS9fft2mqbT0tJAJvgVn3tardZoNIaEhPh8vvKKcs57tqvqU8Ln81Ek9d133/l8PqvVGhYW1oEzQhfyx14/GAwGg8Fgfqf8qQ2YPwaXb3pdrCGnGuEdHMNxXP/+/SmKOnny5AcffCDLMhRccDqdNE2Dg8OHH364cuVKk8k0bNgwNa2DKIptC7Ndcjd+dxAkMXnS5JMnT959993+ud8wmD8SEGIzZMiQiIiIo0ePfvfdd7DUQW5QFMXhcFRWVt6/6P7S0tKQkBA1Z+0VgjgfAcdA+SFFUQ4ePLhz506DwQA5REEzRQj5fL4P1nywbt06m802dOjQP97TCYPBYDAYDKbz/D5yNGBU2s5XB++y+fn5ixcvrqqqevnll0eOHNmZNrvqzRh2KXfv3v3UU0+Jomi1WkeMGJHUI6m5pZkgiJKSkkOHDkFCzYyMjAcffFD9IsdxjIZBP091+ed5XweHDpBa1KwWAcf8eUYD83vH/9ni/5kkSa/XK8vyAw88cOrUqbq6uoyMjEGDBoWEhIiiWFNTU1tbu2fPHpPJZDabH3zwwaFDh0JODf8InV8Yr9dLEMTatWvffvttiqLi4+MzMzNZloWyvgUFBYcPH4ZUprNnz55+w3ScNBGDwWAwGMyfGSw0/JFpaGiYP3++LMv3339/WlpaQLKDX6ADLpdr//79q1ev9nJel9sF2Rm1Wq3RYNTqtP369RsyZEhmZiYkRIQuqYnZ/Hv4ZzCt25pkf05Pe8wflQBBU/3nmjVr9u7dW1xcrNFo6urqGIbRarV6vT44OPiqq64aP358TEyM/zPh113/Tqdz9+7da9eu9Xq9LpffM81o1Gq16enpQ4cO7UDVxWAwGAwGg/mTgIWGPyCq0e7xeHbu3Mlx3FVXXRUZGXklPBcuiMvlUhSlrq6uuLi4rq4uNDTUaDSGhYWZTCYoKyBJEs/z/jnJ2prZfwbTGgsNmD82HfytaWhogEKzlZWVBoPBbDYHBwfHxMSwLAuZGn9TzwSn09neMw3KT16w3hAGg8FgMBjMHx4sNPwB8X8p53ne5/OBv/EvJjSoadgDfihLslp7UpZltSokupCZ/WcwrbHQgPlj097fGq/XG1ArhOd5SZKgaCVU3Ayosol+8bvgvM805KfqQs1jKOSJ71AMBoPBYDAYLDT8AVHNVPUl+Bd+TVcURRRFkiBBR5BESZIlWZah1l3bXT5JlPyTEWChAQsNmD8e7f2tIQiisbEx2BwsKzJU/JUlOaBew29BaPB/pqFzjzWCICCxpf/BkiiJ0s+qdWIwGAwGg8H82cBCA6brCXB1VsUO/wNQ50yFP5tR3Zn78c82Jpg/MLDg1WXfnpTwq4R9BQCleUmSVPvctrfwKxBKfsWuYjAYDAaDwfzq0L92BzB/QKCum2obXFSlDAwGg/kN0nn3BKwyYDAYDAaDwWDHTgwGg8FgOstvofgFBoPBYDAYzG8cLDRgMBgMBoPBYDAYDAaD6TJwjgYMBoPB/Gp0kCTyF+7JRRGQowGDwWAwGAwG4w/2aMBgMBgMBoPBYDAYDAbTZWChAYPBYDCYiwO7M2AwGAwGg8F0ABYaMBgMBoPBYDAYDAaDwXQZOEcDBoPBYDAYDAaDwWAwmC4DezRgMBgMBoPBYDAYDAaD6TKw0IDBYDAYDAaDwWAwGAymy8BCAwaDwWAwGAwGg8FgMJguAwsNGAwGg8FgMBgMBoPBYLoMLDRgMBgMBoPBYDAYDAaD6TKw0IDBYDAYDAaDwWAwGAymy8BCAwaDwWAwGAwGg8FgMJguAwsNGAwGg8FgMBgMBoPBYLoMLDRgMBgMBoPBYDAYDAaD6TKw0IDBYDAYDAaDwWAwGAymy8BCAwaDwWAwGAwGg8FgMJguAwsNGAwGg8FgMBgMBoPBYLoMLDRgMBgMBoPBYDAYDAaD6TKw0IDBYDAYDAaDwWAwGAymy8BCAwaDwWAwGAwGg8FgMJguAwsNGAwGg8FgMBgMBoPBYLoMLDRgMBgMBoPBYDAYDAaD6TKw0IDBYDAYDAaDwWAwGAymy/iTCg2Kovj/U5bli/3KJZ/rd0FnBuRPDh6izvObvQUuYRJlWf7NXs4fHjzyGAwGg8FgML8XqMcff/zX7sMviizLBEEQBOF0OlmWdTgcBEEoikKSpNvtZhimg+8KgkAQhM/no2ka2mlsbKRpWhRFmqbhGEVRZEkmSRLahzdjgiC6/ELgRIIgQOcv5xRer5dAxKZNm/bu3RsUFBQWFoYQEkWRJH/Soa7EJfwWaGho2Lxpc4O9wWq1djD7Ho9Ho9GcOHHi448/JkmyW7duoihSFMVxHE3TsCS6pD9Hjx7dtGmTJEmhoaEajaZL2rwSCILgdDqLi4sPHTq0devWM2fOeDwevV6v1+vbOx4pP62iX2s52e32zz//vKioSKPRhIaGCoLgcDh0Ot15D+Z5nqIoRVF4nofbQZZkpCCCJC6q/06nMycnZ8OGDRRFRUVFXXLnPR6PKIoajUYSJa/Xq9FoOI6jKMr/mD/qfapCEITX6/V6vZIkkQTp/4zCYDAYDAaDwfx26Brr6Bfg1KlTWq3WZDIFBQW1Z8x0BpIkFUUhCMJoNFZUVLjdboRQUo+k4uJiRVF8Pl9SUtJ5DU6CIGRZJkmS47iCggKDwUCSZFxcHEJo8+bN+fn5U6ZMSUxMRAg1tzQ7nc7W1tbw8PDIyMgr9CpMEITb496+fbskSmPGjrFarZfclFarraqq2rBhw759+2JjY7t3786yrEaj+TPsHxYXFz+57MmIiIhXX321b9++7R2m1+u9Xu+2bdteeeWV2bNnd+/eHeQYlmUJgjh27NihQ4emTZsWFxcnSdLlCARbtmx5/fXXb7vttp49e17OOr/StLa2rlu3bs2aNZIkMQxjt9sFQViwYMGSJUvaHgzW70cffySK4syZM41G4y/eX4QQ4nm+qqrq6aef1ul0a9euhY5dcLIIgrDb7T/88ANN02PGjAkODr7Y88qyvGbNmnXr1t19992DBg26xN4jpNVq4WGy/sv1lZWVN998c0REhNfrZVn2ktv8HQGPX/hgMBh+7e5gMBgMBoPBYDridyM0PPXUUzU1NWPGjHn44YcvuRGQGMCFgeO4DRs2bN68OS0t7YnHn1izZs2ePXtiYmJef/319na2KYry+XxlZWWzZ8+OjIxcuHBhXFxcQUHBypUrs7Oz4+Pjk5KSEEIGg+H555/fs2fPXXfdNWvWLJPJdMkd7piqqqrly5d7PJ7wiPBrrrnmcpqSZVkQBI7j6uvrJUnqqh7+9qEoSqvVqibceWloOOvvoCiKwWDQ6/Usy4LTitFo9Hq9//3vf6uqqsrLy1944YXLdEOw2WwGg8Fisfh8vstp50qzb9++d999NzY2NikpKSMjo7y83G63T506tb3jT5069eabb3o8nvDw8IkTJ/6SXVVhGMZisTAMY7VavV6vLMs0TQd4BAQcjxBSFCUrK+udd96prq4eN24cPEMu6ryiKOp0OqvVGh4efjn9hyVaWFj40ksv5eXlxcXFTZs2TavV/uEFQbhAkiRlWXa73eqUVVdX22y2X7VrGAwGg8FgMJjzcwWFBnUDqktobGxsamqSJElt8xLe+CFiwmg0chy3f//+DRs2MAxz0003yYo8fvz4HTt21NTUfPPNNzNmzFC/4v8SDzbkzp07fT6fyWRKTk5GCJlMpvj4eJ7nEUI8zzMMo9Vqb7rppsOHD69ZsyYmJmb8+PGXf/nnxWw2Dxw4sKW5pQNjqfPIsgwSA7TW2tp65SSS3w4cxymKoiiK1+tt7xjwFpEkKSgoCAZHEARJlGBbVavVJiYm2u32uLg4nufB8Ov8yoQYHPWfLMs2NTUhhIKCgjr4VoBt+cs4zPvf0Zs2beI4LjMzc/HixYqigPnXgQtGdHR0jx49WlpaLsEjoD0u9gmgKIrL5YJZ43kefE86aKGlpYVhGL1ebzQaw8PDKYqqr68PuCk604dgczDP8x6P5/Lja7xeb0REREJCgqIoERERv8fAgc7/XVDHVh1ht9tdWFhYXl4+dOhQm80WGhp6BTuKwWAwGAwGg7kMukBo8Pl8LS0tERERHMchhHQ6nSzLiqyQFNmFGQq0Wi1kQ0AINTc3m81mgiAEQSAJkqIp/5dXr9fL87y/PeBvlRkMBkVR7Hb76tWrm5qa5s+f37dvX1mWhw0bxrIsx3G7du0CocHj8ZAkGeCW3Nraun/ffoZhkpOTExMTFUUJCQm58847nU7n8OHDEUI8z9M0nZCQMH/e/KUPL926dWufPn26det2+SPQFqvVOnfuXJ7n+/Tp4/9zWZadTqfJZCIIQhIlkrpABgdZljUajcFg0Gg0ZrMZMuTpdDoIh4bogPN+EUz0zpgN6hSoTUmihBCi6PNLJA6Hw2QytWeTQDoAhJAgCBA8zzCM2nLbc8GRGo0GZCaEkM/nI0lSo9GYTCaKojQaTSf9zyE4nKZpGFVodsGCBVOnTh0xYgR4PUAgvX8/1cwgMLZq/A5CCFpT23e5XEajEa4r4NSwtGBfV5EVhBD0QRIlH+8TRfFyhCHQldQr8nq9Wq22vYOPHDlSUVFhsVgGDx6MEBIEwWg0ejweSZTam1BLqOXmm29GCA1LH3axfVMURRAEhmHU8VThOE6n0zkcDhD4EEIul8tfo4GJBt8EgiAkSXK73aIoQtAEzAI4j8AC8P9sNBjh2TJgwIB58+aRJBkdHY1+/jxR1xiMm/p1WLo+n0+j0UiyBAFfkiT5fD5o3OPxEAQB6SEEQaBpujPPSZZltVrtggULZFkePHgwtAY3+3mPBz1Fq9V2pnFYV7IiK4rCMIzD4UAI+besyjQXpO2TAdYGJJWA28T/eHXhwZE+3ocQOm+3V69eXVJSkpycHBkZSVGU+i0MBoPBYDAYzG+KLhAaWJaNiIhACKk51UiSvKLlLMxms8/n0+l0Go2mubk5JCREEATVUARPeISQJEqcl9NpdST1s6SGPp9v/779hw8fTkpKyszMRAgpskLR1LRp01566SWTyVReXh4eHi4Igmp1q/z444/5BflhYWGjR49GCImiyDBMSkqKenawnw0GQ0rflP79+x84cODgwYM3TLuhPQPsctBoNKmpqT6fL8D68ng8ZrMZPpMUyfM8TdHQAZ/Pp34+L4IgwDwyDON0OvU6fQcmCkEQPM/DtasJLJDfShAEQZZlmqLVKfB6vZBgT/VLb9u+z+dTnaVVw0xFEiX1ejUajWqciJKoZt8871ihc84a8E9FVi7H59zfNEpOTk5OTobWeJ4HgQY2w8EShpFhWRb8JkDEkURJVmRQGTrTEzWihyRJGcmSJMmiTNO0KIksw+r1eliroGio3+rkBrK6YIDz9gfaURSFpmmn02mxWEJDQ1VVRZZl/xstAM7LTZgwAV2MseoPXFqAtwhBENAlMIZBdoTG/fvPMIw6UxRF0TRN0zTDMLC0IBlkgDAEqAqm2WyG+109b9vxgRbUterxeAwGA9wdoiiCciSKIiwPWBvqdwVBQAhdUGuQZRlujUGDBsFd7PP5ZFk2mUw8z8uyrKE1BElIkgTdA4kBBBqWYTt+BMmyLIoiRVKq3d7Wd6PzEydLsiRLMMiwICmKkmWZoijVScFfEARF5ux3FZkgCIhOUhvkOI4giJqamtLSUliBJEm6XC6TySSJEi/wqpykttnJrmIwGAwGg8FgrgSXLjT4v/ELglBdXV1eXo4Qio2NhRSJ8OYKb4Rd0lcV2BBGCNXX1+t0Ov/U642NjTU1NY2NjWFhYdHR0cHBwQHbjz6fj+O4g4cO+ny+a665pm/fvrDnjxAaM2bM22+/3dzcvG3btvnz52vowI0ygiA2bdpkMpnCwsKGDRtGURSYEJIktba2EgQRpA+iaApGpmfPnqmpqQUFBVlZWRkZGbAX2rVA8nmfzyeKIuQmBDQaDXgEIITcbrckSU1NTfX19Qih6OjoqMgL572HLWKHw5Gfn8+ybEJCAjiJtJ1KMJ4VRdFqtXa7/cyZMwih3r17g1czSZAy+plSo9VqBUEoLy9PSEiAYhlttQawlOx2e1hYmCoh2e12j8cDse6wO6rOdUREhM1mM5lMsDPc3kVxHOdwOE6cOBEcHBwdHX35gSEcx/l8Pp1W53Q5eZ7X6XRGo9FfcnK4HAghRsNQNEWSZFNT0+nTpxFCffr00ev1Wq1WQ2rAPCbIC1iYENUCqTQgESkMFFitML+lpaWhoaE2m81isah27HlH+LzXwrJsQ0NDYWEhQig+Pj4kJCRAwAKrmyCI1tbWxsZGlmVdLpfH43G73YqidLzCKYqqq6tDbczXzkAQBMdxjY2NAacAM97lcomiaDQYVUsVrhdufIqiWlpatFqtz+cLCgqCG5YkSXVdmUymM2fOiKIIq8JfB4HrlWXZ4/HAZcbGxHJerm1UC+QOsNvtFRUVCKGYmBjIHQDuM5IkwQwCIHzU1NQ0NDSwLBsWFmaxWNSnZXszJYmSJJ/1TeB5vqW1xWKxqMqaamP7fD71voiPjzeZTNYwq16vFwQBie06ECGEVEMdHmjV1dWVlZUIoW7dunXr1g08hlpbWwMEqfaQFbmurq6qqqqurk5dkyRJwhOS5/kANwTw78jOztbr9ZGRkW1T28JEZ2dnHz9+PD09vaGhwWQysSxbW1ur1+sZhvlTZZbBYDAYDAaD+e1z0UJDgF86z/Pbt2//8MMPW1paIE8BwzDXXXfdpEmTEhMTO64WeTnAe+qunbs+/uTjiRMnzp8/v6Cg4Nlnn3U4HE1NTU6nk6bpqKioGdNnTJ8x3f+llmXZysrKgwcPRkREXH311QRBqMXqevXqlZqaeuzYsf379991110IodbWVqPRSJKkJEoESUiiVFhYKMtyampqkD5IkRWCINxu9+LFi1uaW+bNnzdmzBgKnX2VZxhm9OjRhw4d+vbbb6dPn34lhIbikuKHH36Y5/mlS5f6Cw0PPvhgcXHxAw88kJqaunv37i+//PLEiRNmsxn2NocOHXrjjTempaWpx5Pk2SpxNE1bLBa73b5ixYr8/PzKykpFUViWNQQZbpt92+jRo9sG2IOBeuDAgY8//jg7O1v1PycIYvLkybfccktkZKS/1sNx3L59+5577jlFUV544YV+/fqhNn4N77333ldffRUWFvbBBx+oEfWlpaWPPPJIenr6Y489VlVdtWzZMnWuzWZzaGjoddddN2XKFDCEIEjBv82jR4+uXLmyrKxMEASQpQYPHgwlEsDGu4TxV23mJ554Iisra8KECXPnzgUbyeVybdiwYdWqVfPnzb/przfl5OS88soreXl5cEfwPD9gwIAZM2ZkZGQoikJpLuDtArVXH3300QMHDqSmpi5YsKBfv34wZQUFBWvXrt27dy/DMJAogWEYiqKGDh2akZGRmZnZyV3od955Z9++feXl5Uaj0e12azSauLi4u+++OyMjQz1GkZXPvvjsu+++O378eFhYmNFofPjhhyE2wWQyffbZZx2cq7i4GNK4Pv300ykpKZ3pksrx48eXL19eX1+/YsWKXr16wQ9hfgVB+OCDD7744guapp955hkI5fDn7bfffvnll+++++758+fDvjp4NJjNZo7jHnroocrKyurqap1OR9O0IAj33ntvenq62WwmSdLr9YLz1IEDB1577TWSJJ977rmEhAQEURjnBEqO47788sv33nsPIaQ+A202W8+ePe+//36SJGma1mg0FEVRFBUcHLxmzZqNGzc2NDQwDMNxnMFgiIyMXLJkSVJSkiiKEAvWdhAomiJkAiFUVla2dOnS3NzcBx98cObMmRB6A6Uxdu3atXbtWkmSoB4neBAMGDBg1KhRI0aMMJvNhEx0IPvKsnzw4MEvv/zy0KFDLMuq1xIfHz9v3ry0tLT26oAGcPDgwXXr1h0+fNhgMIAKDJc/adKkzMzMfv36cRzn/0zOysp68cUXW1tbISlGU1NTWlrarbfeOmLECPWYffv2rV69urKy0mazVVZWvvjii3a7HVJs3H777TfccINer//DJ8XEYDAYDAaD+R3RWaEB3uH8twplSaZoaunSpTt37lRdDBBCBEF8+umnp0+fnj9/furA1CsRMqAoCsRX2xvtDQ0NR48ePXLkyD333APmZbdu3ViWrampKSkp+d+z/6M19OTJk/3d77///vumpqYJEybExMT4R0aQJDl+/PiCggKv15udnd2/f3+wzMF88ng827dvLywsTExMHDRokCiJDMNIguTz+ZxOZ25ersfjgbdn1b6Ffbzc3Nzi4uLk5OQuL8mmKEpjY6PL5WpubvZ/yXY6nZWVldnZ2eXl5f/9739pmu7WrZtOp6upqfF6vfv27Tt+/Pirr74aFxdnMBjUbWpIVldcXPz+++/n5uaCa0BEeERxSbGdtD/11FOnTp266667zGazf/S+z+f75ptvXnjhBRB3QkJCRFF0Op0URW3cuPHEiRP3339/amoqOrc/rNPpBEFwuVzgAgCNBOziCoLQ0tJC07Tq9g9VQnw+3/Hjxw8cOLBixYozZ87wPB8TExMTE1NYWOh2u1999dXCwsJFixapaoiaaOCDDz5YuXIlxM9bLBZDkKHB3vDDDz/U19fPnz9fFMVOZppoO/6QkaGxsdHhcIBdCn02GAy1tbU8zx88dDDMGvbYY48FBQWRJKnX610uV3V1dVNT044dO1555ZXMzExFVsCjgfg5av8VRXnmmWe+//57m81299139+rVS1EUWZJLy0oXLVpUXV3NMAxkBqmoqGhpadHpdLt37+7dq3dnllxjY+Mjjzxy4MABj8djMpn0ej1EJJ05c+af//znP//5z2nTpiGEzGYzRVNvv/02xB04nU6Hw8GyLMw4uOh3cBa9Xm+3230+H5ivF0VsbCxN03V1dfv37+/VqxdEoyCENBpNZWXlnj173G53fX39qVOnevTooW65EwRRVlZ25MgRn88XEhIC3YMyExRF1dbWPv3009nZ2SaTKTY2Vq/Xnzp1iqKoF154IT09/amnnmJZlmVZSZTg1vB4PKIowmOE4zitVisrsuATCgsL33rrrZ07d1qt1urq6sjISIRQWVkZ5JEBicHr9dI0DcP1zDPPbNu2zeFwhISE2Gy27OxsjuPsdvtDDz20ZMmSUaNGCYKgipUBKLJC0qQkSXa7XZIkq9UKdy7P8y6X6913333nnXeMRqPRaOzWrRtBEIWFhT6fb8eOHS0tLRMmTCCJC6zw9evXL1++nKZpj8cTFRUVERHhcrnKyso8Hs8jjzyycOHCKVOmdOwd43K51q9f/9lnn1VWVup0usjISIPBUFRUVF9fT1HU119/nZCQkJycDHMEd8pbb721cuVKrVZLUVRUVJTD4bBYLLt27Tp+/PiSJUtmzpwJd1lubm5+fr5er6coyuFwVFdXQ+kQcDmBD+0la8BhFBgMBoPBYDC/PJfi0QB2kazI773z3tGjRymKAhduNTjc4XCcOnVq1apVjz32GKRvuKLk5OTcd999sbGx//73vwcOHAh+9Tk5OW+88caPP/74yiuvpKamhoeHa7VajUbDcVxRURHDMKGhoWrScnVPe/Lkyc8880xNTc3+/fsHDBjA87zZbIYAb4PBsHXrVpPJZDKZxo0dJ0riBd9fwQ2Ypum8vLxrr732So+DCriaf/HFFz6f7y9/+cs//vEPnU4HMfybN29+9tlnW1paXnnllZUrV0JwuCIrgiB4Oa8sy++++64kSbNmzVq4cCHLsj6fT5KkFStWrFmzZsuWLd26dbvpppvAQoBKDadPn3744YeNRuPQoUMfeughm83Gsmxra2tWVtaTTz5ZUFCwfPny9957j2VYhNB5M3d0xrEfVACCIOrr6++55x6SJF988cVRo0aBzVlfX//cc89t37597969Y8eOHTJkiF6vJ0lSq9V6vd7S0tLnnnuOoqjMzMy7774bfCgEQVizZs3q1asXL16sRux3yeArsuJ/mT/++OOePXv69ev34IMPwk4+eLY//PDD5eXlzz77bGpqarD5/IUYtFotXOBjjz22efPm8PDw+fPnp6SkaDQaqG/y1ltvlZWVDR069OGHH05ISFBTA+bk5GRlZQ0eMhhcS9qTAGBhL126tLS0lCCIhx9++IYbboBYJ6fT+dJLL23duvX555+Pjo4eOXIkfGXjxo06nW7v3r0333xznz59FixY4O/ygNqfTcWPix1So9EI/ji7du268847dTqdKgxVVVXV1tZCIsxvvvnm1ltvdTgcBoMB9NCqqqqioiKbzRYfHw+DoHo0LF261OPxPP7449OmTQOJ0OfzPf300zt27Ni5c+f48ePHjRsHZ2+rQOn1+rNZHiVp9erV27ZtCwsLmzZt2pw5c0xGE0LI4XTs27cvMTERRhiSWZpMpg0bNjQ1NY0cOXLx4sUmkwlEt3379i1btiy+ptQ3AAAgAElEQVQ7O3vXrl0DBgzooCqHrMjn1SBYlt27d+/7778fHh7+n//8Z9SoUSBwIIRKSkoOHz4Mfi4ByTvaEhsbO3bs2KSkpKuvvhocN1wuV2Vl5T333FNdXb1x48bJkyd3PFMVFRUHDx7My8v761//et9994WEhMCatNvtJ0+eLCkpycjI0Ol0kAuDJMldu3Z99NFHkiRlZmY++eSTGo1GEITa2tqlS5ceOXJkxYoVkyZNAveHu+6669Zbbz158uQdd9zRp0+fZcuWJSclI4QgM8vZ8fl5Ph0MBoPBYDAYzK9IZ3dx4SWVJElJkiAxeUNDw+eff97S0hISEgIba+icOQEG0s6dO/fs2aO2cIX8WiHBu81mW/vh2p49e/p8PvCh6Nu376233mo0GqurqwsLC1VL0uVyZWVlabVaiGVQ9RGNRsMwDEmSU6ZMqampOXjwIDqXIQ/eX8vLy/Py8jQaTf/+/TvppmE2mxMTEzUaDbR25QYhAJ/Pp9VqJUkaO3bsQw89pNfrdTqdz+drbW2dNHHSnDlzFEU5cuRIZWUlJFOkaIphGIZlWJZ1u91///vfQWWAuAm9Xr948eK5c+d6vd6tW7cKggDyhCAI9fX1a9eu9Xg8w4YNe+6557p37w5nNxqNw4cPf//9981mc05OzkcffUTRFEVTLperM/3/ySQ9l68R/qXRaFwul8Vi2bBhw6hRoxRFAVMtJCRkwYIFaWlp+fn5R44c8W+HJMmXX36ZoqiUlJRHH300KSkJeujz+e68884nnngCsutBAP+VmAuPx9OtW7fXXnstJSXF5XJ5vd6QkJC+ffsuWrQoIiKisbERciK0912dTvfSSy+tW7fOZDItWrRo8uTJLpcLzFe73V5VVaUoyuzZs202m9frpSkaCkBkZGT8/e9/T0pKYlkWHEPO276iKF9//XVJSUljY+OaNWumT58OlT4URbFarU8//fT999+v1Wqffvrp5uZmuH3AlYBlWYZhYDYh84Xqp3Aldo8pkoJwhlOnTsF6gBWIEMrOzi4uLp4wYUJoaOjBgwerq6tNJhOE6zscjoKCgurq6gEDBvTo0UO9ZICm6TfffHPixIkIIdgn12l1//3vfyEzxerVqyFtJ8RVtb0okiQVWTlw4MDXX39tNBoXL14MdjWs85CQkIkTJyYmJpIk6fF4KIrSarV6vT4vL+/2229/5plnIiMj9Xo9aEDDhw//xz/+ERUVdeDAgfz8/EsYH8j44Ha7p06deu2113q9XpCiEELdu3e/7rrrZs6ceUGVASGUlpb2zDPPzJ07NyEhwev1QjaQXr16LVy4UBAEt9udl5fX3ndhXhwOR15eXkxMzA033BASEtLQ0IAQ4nk+LCzsmmuuueOOO4KDgwVBANGT5/nnnnuusbHxuuuue+ihhyD7I8uyoaGhy5YtS0tLKykp+d///qfRaIKCgmRJBv3L4XDQNG0wGGCo/VUGiGXrzJViMBgMBoPBYK40F+0uTlEUSZCyIv/www8Oh0NRlLZZuGADTZbljRs3dlE/26WqqorjuJkzZ1I0RZwrHwikpqYmJSVBFQmaohVFkWVZ9WgICQnxbwc8GgwGw4QJEwwGg8/n27dvHxgbsCe5c+dO2F6eNWuW0+lUc6fD3ul5t2qDgoJsNptGo8nNzUXnjJwrPSDoXP4/rVZ72223aTQaqHEAkgFFU9deey1FUV6vt6qqSv3KWbtelq1W66RJk2BjHK7R6XQqinLNNdckJSX9+OOPO3fuVK+iubn5+PHjycnJ119/PZig8BVwY7ZYLDNnzmQYZtu2bfn5+U6nE2qLgil7CUPhcrl0Ot21114LmfagBQj/Dg0NHTJkiM/nA4tLHYfCwsJvv/1Wp9ONHTtWr9dDgk+aomF9jh49OiYmBlL3QzG/Lqe1tfWuu+6CFQVJHMFITklJgaoEkD7QH9VM0ul0b7zxxooVK6Kjo//1r39NmjQJIRQSEgKh6WBxwXoGr36KpjQaTYCsoCbgaIuiKO+++251dfXQoUP79u0Ls2MwGNSQlmuuuaZnz56FhYWbNm0CFQN+7vV6CYKQJMlkMkGRF9Vpor1pvRyPBs7LjRgxwmg0Mgyzd+9ehBBFUj6fr6ysLC8vLyQkZPr06bGxsQRBfPfdd+icMuh2u6uqqmRZTk5ODgkJgXtZkiSo/tCvX79hw4bBtj+kh5QVGSE0fPjwxsbG+vr6Dqp7QjlYzsutX7+epunx48dPnDgR2pdlWa39AdqlVqvleZ4kydbWVq1Wm5mZCQ4OEEUiyzLLsn369LFYLI2NjWpS24sCjPagoKCWlpaqqip4slHk2aYgfKZj2xv6DAEI8BPw/4LJGjFiBMuyJSUlBQUF7bUAawMkG4hu4Hk+NCRUEASv18txnOqEcrZMjCStXbsWxvnf//43ZO31eDw8zxuNxpiYmLS0tMjIyK+//hpyzUI6VagSqiiK0+kEJQWq/EiiBAVfMRgMBoPBYDC/ES5aaCAIwu1xazSakydPchxH0zTknPe3H+Ct2mAw5ObmtrS0XFHr2mq1iqJ41VVXybLMMAw4OMB7M6NhwJGhpqZGkiUw80AWYRgGig6qO2AkcTbNRFpaWkxMTH5+/okTJ8AkgI2yLZu3cBwXHh6enJwMyeRVZEmGxPIBfVMUBV6avV5vUVHReUUZqAeJEBJ+Ds/zgiBc8tBBiriEhAR/Aw+cFGJiYnieDw8PLysra9t+eno6wzA8z0P6BlmWg4KCFEVJSUmx2WwURR08eFAVWY4ePQrR5v379wd1A4wrRVGgCOW4ceN8Pp/L5Tpz5ozRaHS5XLIkq6jtdPKiGIaprq7u168fjIwq8UABQkjCD1U21K8UFRUFBweHhISMGTNGp9WdLVhIkWCrg4ACgT9XKGu9TqeDEqpgZcESRQiFhoaazWZRFMvKyvyP93g86FydxW3btr355pvx8fELFiyARAn+JCQkdO/ePSQkZMWKFT/++GNlZWVra6skSp1XTJqbm6FgCkgY/qoZTJDNZrNarcHBwYcOHaJoSjWDITZB5UqXFTQYDD169AgKCnI4HCdPngSblmXZpqam77//HkqcDBs2DCG0a9cutZyEw+HYuXNncHBwYmKiwWAIEA5SUlKUc+VIaZoGHwdFUWJjY0EaUC+HJEhQMH/yspEVhJAsy0ePHpUkqUePHpBiAPnJOtC4ursO+RqHDRsWFhZGnKsDov6WIIjg4GCn02m32zsYB0iy4D/y8E+WZXv16hUcHPztt9++++67lZWVXq+Xoimn0wmpOjseXkmUQGZtG0AkiqLD4ZAkCfwv4K45byOwBkwmU//+/evr6996661du3Z5OA+IMjC2MIZwvEajOXz4sNfrHThwoE6ng8cdTdM8z9fX1wuCEBkZGRoa+v/snXd4FWXa/6edOb2lnDRSICEVSAIIgQiidFxEERUVvXSRV11dXcXX1XUVfd21Xq6u4vpTLIvgCupiAQRUMCChCEKoSUgnvZycXqf9/riT8XBSSEJE3b0/l5dXnDPzzDPPPDPO/X3uEgwG4V6AFw9oFvK1y/5rNENTNBVWxhi9GxAEQRAEQX5GBp2jgeM4rUZLEERbWxt8xkEuvdDvOVEUIyMjwUF9ODvbB2q1eoDp0AmCaG1tBY90i8USuh1MUIIgNBpNQUHB4cOHy8rKnE5nRESEKInHjx73eD1KpfL2228nQnwT4A+h23ru/9RyIEAokHPxxIkTYfYhRVGpqalglgzw0kKPhfz2UDBvIIfIH+VarVapVNIU3bOr4NvsdrvhSkVRtNlsBEFoNJqwOogEQcBHv0ajSUhI8Hq9XcuSNE3+qO2QcuaFgV8aJJsY+P6dnZ0EQTAMkxCfALcYcijIJzWZTGAiDnCgBotGo4Fxk90B+gesULVavW3btv/3//6fKIqFhYVLliwJ3UeSJKjsuHjxYrvdvm/fvvvuu2/69OmTJk1KS0uD8JCB0NDQ4PF4dDpdUlJSX/vExsZqtdr29na/338hRWQuxKMByMvLq6io2Llz5z333ANKVkNDQ0tLy+zZsw0GQ3Z2dk5OTmlpKcTCQGRHS0vLqFGjQtPEUN0YjUZZAhhsTyBzJyhoGo3GYDBIktRPqkWKosBrBoKYZA0CJAzY4QKt4vT09Hvuueett97atGnTqVOnJk2adMkll+Tl5Z13tAVe4HgOPH04jvN6vZWVlY2NjSRJ+v1+h8PhcDg4jouOjnY4HBAK0Q/JyclLlixpa2srLy9/9tlnCwoKrrjiilGjRqWmppIkyXEcSZKgufj9frfbTdN0p7XzrbfekluQHUCOHDnS2dkpiuIA463I7ti9geyMIAiCIAiC/NQM/iObJGmG5jguPj4ePtlhNQxMCHmhCTwLRqcN1Oa5ECBB4wB39vv9sNrfU5uABOYEQUyZMmXjxo1lZWVHjx6dPWu2KImbN292OBxxcXFz584lCIIkSYZmiO4k9pBebiBuz6GjBPA8397e/uabb5aWlobuGR8f/7vf/Q7yXwzw0mSgPxaLBa50UMeaTCawJ+UykDIajYZhGIfDAQUjIDKcIAiDwaDX68GHWQauUaFQREREnD59GpL2k+fSj0t/9+KxSEo/LrMTA7vXsg4iCAJkMWBZFlSGngMSExPDMAzP832lrL9AzGYz+K73NIGkkBouMrD8fvr06ePHjnd2dqrVap/PJ9dZAGDoAoHA2LFjn3766ffee+/48eNHjhz56quv0tLS5s6dW1BQkJiYGBUVFVpspSednZ1+vx8qR/S1DwQsWK3WIczDYUSSpJkzZ27fvr2ysrK+vj4hPqG5pXnHjh1GozEvL0+j0UybNm316tVut3vr1q2LFi2SJOnMmTNOpzMtLW3UqFGQ7ZVlWZ7nBUEQBAHKwQYCgf7VE5L8sfytDLwovF6vw+GIioqKioqC9fa+GhEEwev1iqIICSDgjtM0LTcLL1Kapoc8yAaD4aabboqJidm+ffuePXtOnDjx7rvv3nDDDemj0wsvLUxMTOz1KFEUBVFQMAqO5+wOu9VqPXjw4M6dO8vLyz0eDxSwgCCjmpoag8HQjw4SDAbB36GgoECr1W7ZsmXbtm1fffXVp59+OnXq1Msuuyw7Ozs1NdVgMID6Cc5iNE3b7La1a9fK7cD72e/3GwwGnU4XHR3d3t6enJwMnhFygpi+QP8FBEEQBEGQXwiDFhogcJdhmEsvvXTr1q2gMoQBViVBEJdMuoT4iT/+JEkCt96ehpwoidK5kCSpVqsZhoG1u56tgQmRm5s7bty4kpKSs2fPwvbvv//e4/HMmzcPSloQ3auaYZbzeXtLkqQoiKEuvuCOERERAckpZVQqFfgqO53Ogcso8lnAo6GnrnFe5IKIPcdTpVJRFOVxe8CZWfZo6MudROAFj8cD+fD8fj9FUhIlQd9gCbcfs4rsTrcB+4AlBrcPUk70cwmhPbdareBJIasM8sonjAzUIBzsKA0cOcdkT4cXCNkI297W1ubxeEpLS3NychYtWlRUVLR169bY2Nj7778/dDeFQgGjyjDM73//+7KyshMnTpw6dWr37t3vvPPOF198cd1111111VVhkyoMKDABIyNvJLsd+0VBJCkSDD8wyC8kq/+P4tGQGhEFEXzsDQbDsWPHYmJizp49u3379okTJ4IVTZJkfn5+RUVFUVHRDTfc0N7eXlZWplAoEhISoqOjwQMfgnoAyPPaq9YzEDiO8/l8Ho9Ho9Ho9XpQMfpqB+ovgkdDl8pA0fCGhFvAcRzP8/DvAXYg1KkKCmfyPH/F5Vfk5eXNmzfv6NGjR44c2bRpk06nK95XvHz58ry8PPnAUIEDdBa7w15RUfH666/X1dXFxsbecsstSUlJSqVSTtPwpz/9SalUgrraa09khwWO48aOHZuamnrFFVeUl5cfOHCgsbHx5ZdfHjly5PLly6dOnQpag1qt5oIcy7KjRo0KDQvq6o/dDukempqacnJy5LGFzBEECgoIgiAIgiC/eIbiLg4febNmzYKCgiRJwhK30+n0+XwURUFsrUajueqqq/Q6/XkbvEB4nne73ZDKbiA7EwQB4b697gC5zWfMmLF///7Tp0/bHfaTJ0+SJGk2m2+99Va53oQcIB3mEx7ah2AwCGHSKpXKZDJBFDHP89S5qTECgcCKFSvsdnuooGAymWA5Ghyz5e0D/MIWRbGjo2MIC6SVlZXgkEJT9Dn51SjC5XLRND06fTRU4FMoFBkZGSUlJV6vV5KkUPUE6LR1QiJMs9ms1+tphibFLkdxgiAEQZDzUxAEAXcQ/q6pqYE/5FXiruoYNC3/IdtL8oCA1uNyuWSFgiTJcePG7dq1C5LMyWIQlOeEk3Z0dIA790/kdA0xNRRFhUV8kCQJQkPYecEizcjIWL58+bRp0yIjIz/44IMPP/wwMzNzzpw5JEmC1AViH+gmkO8wPT396quvnjlz5nfffbdly5bVq1dLkvTb3/62n6SGBEEYDIaysjLIZAG2N/RBqVSSFElRlNPp9Pv9cXFxEEJPEIQkSQ6HY7Cu/n25sbhcrrCMJ70CVQwtFktLS8v+/fsXLlzY1tYGPgITJ0ykGToYDE6ePHnz5s2nTp2y2+16vX7r1q3p6emTJk2CWy/nRAQgayaEDAwWcN4xmUzx8fF2u720tHTixIkwaL3uD6FkIJbByj88CzABiAEnFABxEya2nO9AFk2A6Ojo2bNn5+fnT5ky5fjx4x999NGuXbvsdvujjz6anZ1N9Kg/Cr4YarV6w4YNu3fvXrx48e233z5u3LjQaQnil0KhiIqO6qVXJAlaniAIFEkpWSW0OX78+PHjx0+dOrW2tnbTpk27d+9+4YUXbr/99uXLl3cFqWk1oLNAihBJknw+X1+BUdBn0DrNZjO4KQ0wHAlBEARBEAS5+FyQO/RDDz00cuRICOKFb0RYH6ZpWqPRPPDAA3FxcQOsBDlk+rEPw9wZwGyOiYlhWbatra0voYEgiKSkpIyMjLi4uLKyMkj5Vl9fbzaboXzjwFGpVBCKDLknwZAIEyYIglAqlQkJCdlZ2UlJScndGI1GlUoVutvAkY8iSXKwydhdLhcERYfduGAw2Nzc7PV6ExMTwf5nFWxiYiJUN2xoaJDz58lEmCOamppkTwQpJO18aP4OkBu0Wq0cfGG322UtIPSK5JIB/SdulEdMkqSIiIiOjg6O4+TiDhABDuaWXq+HgiA/UYKGnh0LvfW9Cg2QfCE5OXncuHFKpfKaa65JT093OBzPPvtse3u72+0WREHqLrcRZqYqFIpLL730tttuu/rqq2maLioqcjgc/fQnMzNTFEWj0VhdXW00GoPBICQKgbgYGP/W1labzRYTEyOLPsOryAxEZZDPO2PGDJ1OV1FR4XK5jh8/HhkZmZ2dDeY3y7IFBQUWi8VqtR4+fLi2tlYURYhrILrTKIYhCAJFU0O4nCAXBE0BIona29tFUQTxK+zRBuQ6FKEbIVYCNoKPz6BCJ2TfkF4f8KioqKlTpt50001//vOfWZYtKSmpq6sDH66wU0AhzLq6umPHjiUlJRUWFqanp4f13+12wzX2NZ3gKuRBpkhK/ic5OXnWrFn33Xff1KlTOzs7bTYbVIgIBoNQRFN+dfA8f970KzDIoN7Cfw5stBAEQRAEQZCLzQUJDUaj8d133126dCl8W0PCf5PJFBcX98QTTyxYsCA09vsiOLsOZGHQZDIlJCS4XC45LKIngUAgIyMjLy+vtbV13759x44do2ka0toDYfkI+qG9vT0QCCQnJ/f/DU1RFJTnlLf0ZbT0RV/XDmbYwCkuLgZTJNTdALbX19cbjcZx48bJLefm5qpUqsrKyn379vWMdff6vDt27CBJcuzYsVlZWWBUC7wQHR0NJlZJSQnsCU7X4BNx5MiRtrY2nufBioDLh9hsUBngj35utDxiFEWNHj06KSnJ4XAUFRX5/X6O4wRBkLod5r1e7/79+8FKHMYcBAO8ZVJvfvuCICgUCp1OB8+O0Wj885//nJKScvbs2fvvv1+hUPTvXS8KYmRkZF5enlKp9Pl8PM/3k5A1Pj5+3LhxOp3us88+83q9UB0gNGLlhx9+qKmp0Wq1c+bMGcgV/XTAhc+bN0+tVjc1Nf3www+HDh0aMWJEYWEhdDUQCNA0nZmZCTURT548KQhCQkJCQkICSZKDfQr6B2amSqWaMWOG1+s9duxYfX39T5TjY+B0qamiKIqiwAuiJEI5mLS0NIVC0dDQ0NcrSBAEl8tls9n0en10dLRKpQoLK9u+fbtCodBqtUOozALDMmrUqLy8vM7Ozs7OTpfbBWVfZs6cqdVqrVbrtm3bIAXDANvs7OyEErY/b94QBEEQBEEQpB8u6ENNqVSq1eqHH354w4YNTzzxxG233XbHHXe88MIL69atmzNnDhQtG66OhjI0zQLsWEmSkpOTaZqurq6G7T2DxlmWjYqKysrKEgRh8+bNFRUVPp9Pzvwv8EIgGJD9lsMMRTkFAEEQNputvb2doqiMjIxeOy//LfBCMBgU+B9rPw7Nl2GwyKcI7dXatWvlqnIy27dvdzgcycnJoUUN0tLSrrnmGpfL9dlnn8mqgUxtbe2XX35J03RhYSG4bfM8TzO0xWKB9Ps7d+4kCEKtVguCIPBdCQv+/e9/OxwO8GiAHH6QRCDUnWHgoeypqamXXXYZ1EF0uVyQBBRcNnw+3759+8rKysCzYMhGS69lTYfWFAREcBzndDqtVqsoigzDxMXFrVy5MiIi4syZM6+//no/ipXVaqUZWqlUchxntVotFgvDMP0IDaIoXnbZZSNHjmxoaPj22285jtNoNODuAbFFmzZtamtrGzVq1Pz584d2Rb0i8IIs1T333HO/+93v1qxZ078bPPQqKioKbN2DBw/W1dVFRkbm5OTIOyiVyhkzZthstsOHD0Pp1sjISKPR2E89iCFAUl3lPPU6/Q033GA2m2tra7/77jvIV9JX589pYfgkVwhZEnihS2UQxK7HROjy+tHr9dXV1RaLxWg09pUdg2EYg8EQGRnZ0tLS2dnZM+HiunXrEhMT4ZkdQg8JgmBZ1uPxmM1mqOoKEuH06dOjo6IJgnjnnXdAkO3HyywUt9vda4YdBEEQBEEQ5JfDBX2Cy3GzOTk5V1999cqVK++5557CwkL5e/RC6uGF4ff7ZdMX1qIDgYAgCLBG3Wt0tCRJYMSyLMuyLPSWZdkJEyYEAoGmpiZwaui5TAe22ZgxY5KTk0+ePBkIBCZMmBATE8NxnCiKoiSGWQ6dnZ2QqEI+HJbcBUGA/O2jR49Wq7rGhCIp2du5y/lZkmCJj6JDaj8OwDsDolSCwWCY4R0MBv1+P8/zNBXuWgwNwvI1BDVIkhQMBhmGiY2NpWlap9N98MEHjz32WENDA0mRoiRWVVetenLVrl27AoHAvffeGxsbKzelVCqXLFliNpurq6ufe+65LVu2NDY2chzX3t6+efPmu+66y+125+Tk3H777aFGjsViueaaa3w+X3l5+WOPPebz+YLBIM3Qp06d+uMf/7hr1y61Wh0IBJxOp+zsrVartVotwzB6vV6n0+n1+rBhAXtGrVbTNB3qUM1x3C233KLRaKqqqpYvX7579+6Ojg69Xu9wON58883nn38+JSXF4/F4vd5+DHiBF7xeL7hdQEIQt9sN/ggwhgRBeL1eOeREEAQQR0wmE0MzkihBOsBQLxWSJFmWhYIXBJQ+FSWCIDQaDZTAkHMrMAxzxRVX3HzzzQzDfPzxxxs3boQznjp1asWKFa+++uqxY8fgdkRGRnq93g0fbnj11VdVKtWUKVPMZnM/mUQpipo7d256erpSqbz77rtffPHFEydOwOwtKSm57777tm7dqlKp7r77bp1OB6k9OY6TRAkCYZRK5QAtQ4Ig/H4/3Bqfz8cLPC/wBEEcPXr0yy+/3LJlCzhD9d8CwzAsy44bN87hcGzevJll2alTp8oPPgS/zJs3D+ojfvLJJ9nZ2eB9E1o2At4V/bjcgz0cCATgcYYQEqVSGQgEPB6PnKUCntlJkyYtXLiwqalp9erVq1evrq2thXqQtbW169ate+qpp+D5UigUDocD4qdompafSuiV0+mECQM5UPoZAZDe1Gq1x+MhSdLn88GcFCXx9ddf/5//+Z/tO7ZDoBb4sxw5cuTOO++UJMloNM6YMQPeWj1VMPBWSEtL02q1GzduBHHQ7XZ3dnauX78ewlW0Wm1ra2tftxvefrzAHzp86IalN6x5e83p0tOiJAqiAG+bd955Z/369TqdLjY2Fq4amnrofx+Kj4/v7OxcunTpJ598ApV3rFbrsWPHdu3add999/3tb38DTcrlchEEYTKZLpl4SWtr6/r16xsbGwOBAMdxdru9v3mDIAiCIAiC/BxcjND0YYFl2cjISDB4glwQzMKIiAgoJeDz+cI8bzmOUyqVwWDQ5/P5/X7Izw95BNPS0kiSbGtrg4j0ni678CmckZFhsViam5spirr88svBIARziKZpSZQIqsvkiIqKqq2tDc26BztXVFRAhcKcnBxIedAVrU2dEyIx5DGB5O1GozFMZ1GpVCqVSqlUQk3H0J8g0aNKpbLZbH6/H66UYRilUmmz2ViWXbdu3fr169euXfvNN9+43W6TyeTxeAwGA8uyK1asmDFjRlgfsrKyXnzxxVWrVp0+ffqRRx4xmUwKhcLtdmu1WpVKNX78+Icffhj2hAgFqNT4+9//vri4uLy8/LPPPvv8889NJpPT6eQ4zmw233/f/aySfeWVV0LTQwq8ACairC6F3rVAIEBRFM/xbrcbYivknzQaTVZW1uOPP75mzRqn0/n73/9eo9EolUqv1ysIwsSJE1977bUJEybQNN3R0ZGcnNzrOHt9Xr1eD9krYJQiIiJg6DiOY1lWp9OBCEJTtN/vB/WBZdmWlpaUlBSCICSxl6oWoigGg8HQtfYw9CEAACAASURBVFlJlGCIepY1feCBByoqKr755pstW7YkJSUVFBRoNJpAILB27dr33nuPYRiYfjabjaKoqKioWbNmLVq0aCBK3zPPPPPss89+8cUXmzdv/vTTTwOBAKTGdLvdWVlZ99xzz/Tp0wmC4AUebgeYiGDowiM2kLPIegf8AaPR1tYGmTLGjBlz3kZgQObOnbtz506PxxMXFzdixAj5V3l4Z8+e/fbbb0dERFAUdckl51S9EXhBqVSCzU+SJEVSof5HgE6n8/v9KpWKZVn4Q+CF1tZWrVYL6TAhiYa8/4MPPuj3+3ft2vXpp59u2LABfoKnDEp+OJ1OjUbDsqzNZvP5fDRNh6VNhVyMer0e5Ib+B4HjOI/HExkZGQgEIJ8lCBmCIBQXFx89epQgCLkPgiDQNJ2amvrEE09YLJawgQJA80pMTLz11luff/758vLy//3f/01ISPB4PPAUJyYmPvLII19++eXmzZvP64sBaT7+/ve/v/baazqdDiZMZ2cnRVEJCQlTp05dtmyZwAsut8tkMvn9/qysrIcffviVV16pqqpavXp1c3Mz5OyAOwLqpyAKQlCA5z01NXXF/6x4+OGHv/nmm0OHDmVkZLS0tBQUFLz88sv9a4UIgiAIgiDIReZXIzQkJyc3NjayLEsztIpWEQQB1RzS09MtFgv4GoTaZvBhmpSUNGbMGKPRKH+DajSa3NzcrKyskydPfvfdd1mZWZCyPtTOgUp+FovlyiuvrK2tjY2NnTlzptls5jiuVzfsmJiYwsJCuZSgSqUKBAIOh+PEiRO1tbW5ubnyT5IoDWO4eDAYtFgsBoMhLKOe0WiMioqC2hByvkAwHeHTf8SIEWazWfZNgJXSUaNGgRZz3333paam7ty5s6Ojw+/3BwKBMWPGLF68uLCwkCAIsL7klj0ez4QJE/71r3+9++67P/zwg91u53kezr5o0aKFCxcS5xpysBju8/k2bNjwzjvvfPzxxxzHQXC4Xq9fsWJFQUFBU1NTenq6QqEAQcHtdguCoFKpIiIiYDk3TBsiSdLj8bBKNjc3NyYmRr4umfnz5+fl5f3jH/9obW3t6OhQKBQpKSkTJky46667FArF/Pnz29vbz5uVUKFQZGdnjx8/Hhz4Yc5AJ1m269Q0Q9MMzXGcw+EwGAxxcXEg9xAE0VP0SU5O9nl9SUlJoRszMjLGjx8Pyo58dfDHE088AWn5Tpw4kZubGx8fv2zZsl27dtXU1NhsNqVSKQiCWq1OT08vLCycP3++VqsFLaOvwhMulwtWvx999NFLLrnko48+amlpoWna7/fHx8cnJiYuWbIkOztbEiWKomDMBV6gaTomJiY9PT0jIwOKsPY/bgBN03FxceA7A940kIOwqalp/Pjxubm5Awxdyc/PHzNmDE3TCQkJWVlZsBHcJaCHCxcuLC8vJ0kyLy8vMjIydAAFUdDpdPn5+V11VXrLUxsXFxcTE6PVakEUIAiC47mEhASLxWKxWHw+X3R0dOj+er3+2Wef3bRp044dO1paWsAZSqlUxsTEzJkzx+fzQZVZi8WSn58fHR0tF1gluhN/KpVKg8FAUdTYsWNFUYS+9XrtINIxDBMZGcnzvNFoVDBdCUGXLl0qCMKhQ4d8Pp/cB5IkZ86cuXTpUsiI2ZPQrLGFhYWrVq3atGnT4cOH9Xq9Xq/XaDSXX375tddeCzkvS0tL5VqtvaJUKidNmvTggw9+/vnnDQ0NHo9Hq9WC31NOTs6sWbOmT58uSRLN0PBq4nke/G4mT578yiuv7Nmzh6IohmG0Wq3ZbNZqtQUFBdOnT2dZ1m63wyEEQcybN48giDfffBMmqtFohPqmqDIgCIIgCIL8ohhKDfmfBfBFp0gqTBdob29Xq9U6nS5slZvortwWJkDA9rVr165ZsyYuLu6ll15KTU0NBALQYFg6xp6rf6Lwo60IpiO44oP9DL/SDO10OhsbG//0pz+dOnXq/576v6U3LoVL6Ck0DDZgeyD7OxwOqHgn8AIsn/Y8KvTqAoGAKIpKpVJOAwnBC0qlsqWlBex2p9PZjxO+TENDg0KhgDATCMoIKzsKJwUrThAEVsF6fV673Z6QkEBRVCAQYGjG4/WwLAuRL0S3tAGpMZSsEiodhp4UohJ6BpB7vV4IFZEtbYEXmlua4+PjKYoK9ZiAooN9ja08tcCjXq/Xw6QC73qyu5KIUqm0Wq2RkZGiKHIcx9BMWFfDhAaPx6PT6UiSDM0tyvEc9DbUCJT/DUMBN8Xlcmm1Woqi5KD6YDAIAfAEQbS1tVkslv7dDbxeLxRDcTgcOq2OZmiXy9XZ2SmKYlxcnIJRCKIAOSMUCoUkSjCxod4qSZJQiVPJKvuaYz2BRwAsfLfb/fLLL7/zzjsPPPDAihUrBmgodnR0aDSasJ3BKwQ2tre3h2kBMjAaMIbBYPDHEAbqRzFOflfI7w24y2HDKE9puSKj2+2maRoyNUD9RdDv4FcYw666oSRJQFoWLgiTFqQBlmVFQey/TA8UNyVC5qSMrMpBKIHJZNLpdDDJe74DQwEvGLVaDdfodrvr6uqysrLgEHm4SJLsP2cqVCqFbvh8PpfLBRVeIiIiQqWuQCBAkiRN0XAhkKIVZD5RFJubm1UqlV6vDx1wWdwMfdLtdrvL5TIYDPCu8/l8oQIogiAIgiAI8vPyqxEaQvt53s/xXo8N/QCtqan585//vGfPnmeffXbJkiU0TcsFDnt+p8of94ScVUGUSIqkKApMRFk7AOdnSZJ4nv/ss8+ef/75jIyMp556auTIkRqNple/6J9CaBgCsiTR13zoNYflAOkpNPTc3hd97R+6HXzaex4FZi3RHaU/LEMn96ErG6goEQQByTV67Wdof8LmcK++LXI75LkFKeTtXTkezi1qGNoUSZKiIAqiwLJs6NTt/1okURIlMdRhJ3Q+yMNIdPu/hG4Z2tyoq6v74x//ePbs2VdeeWXMmDEDFBrkJKyhpwsGg5IksSwLWuR5bzccLmc2gRoo5+25/BoJvS89xaCBNEIQBNwjkLfkN8PA2yF6U0JDT9Frktqefw8XPef8OaMUMlt4nmcYBl6hA+yV1F2fBcYK5ir8BDP2QuYhgiAIgiAI8lPwqywPNiiVgejOgBi6JSUlZdmyZZGRkTt27KipqVEoFPIC8sCVl57CAU3TSqWSZVmfz7d582a9Xn/99dfHx8drNJpfuKATFjd+cSBD6Guf0ASKfe0TpjLAUaF3RxIlURAv2i3o1X8k3PA7XwTNhfSWpEiaosFNfUD7kyRFUwzDUDQl/xPWoNzhXqtsDKG3LS0tNTU1c+bMiY2NHXJtSDg1QzMKRiGJ0qAKTNDdDMooHa5ZBAMeNlHlU/zCXxcXAjytg4og66m/UCQF/4Tu9p89bgiCIAiCIL8ufjU5GoYR8KCeOHHilVde+emnn37++eexsbFyDHBPwpaLCYIg6a4F6p4raQRBQJb1EydOLFy4cPbs2WazOfTYXzg/RSd/lgvvOmmIJQKxLWHeBxfe/tBaG/hRPfckSTJMIQzbhyRJkib7WvEeFH22QPX7a7/YbDaz2WyxWG699dbCwsKYmJjBCg3h56UIIiQBygB7NZzT4JfHz96xczpwAbOll2YpItyj5+e+WARBEARBECSMX2XoxAV+VkL2AY7j6urqjh07FhcXN3nS5NAl3LD2QyOce3pNh+3P8zzP88ePH5ckKTU11Wg0siwr54C4cH6iT+pBTYOL43o98D70c2xfUQzEUK8irEHZmO+ZzmMIjfdKry334xjf/8aeTQ2NnuFIAzwp0T1ocsALZHUd1Hn733jeAIReG7nw4RpU6ESvDQ7K+b//0Il+uPjPb1+zJZSBB4z0DMro6ZCF0gOCIAiCIMjPy69GaBhGeg1c7+dTuOcHfT+DRpJkP+33uv9A+nzR+LX087z0ZUwOyzp/WPtDaPBCxrn/Z3Z479TA3w8Xc4b0JTcMSkXqVSgcWk+GRbSSGfb5OVxtXkgHemXID04/T/Gv7k2FIAiCIAjyH8mvMkfDBTLYFA+/tPaRCwHtkP9U8M7+9xCqKOF9RxAEQRAE+QXy3+jRgCDIfx7D5aty0fiP8R66+AyLNwqCIAiCIAjy0/HfmAwSQZD/PNDm/O8B7zWCIAiCIMgvHPRoQBAEQRAEQRAEQRBk2PhvzNGAIAiCIAiCIAiCIMhPBAoNCIIgCIIgCIIgCIIMGyg0IAiCIAiCIAiCIAgybKDQgCAIgiAIgiAIgiDIsIFCA4IgCIIgCIIgCIIgwwYKDQiCIAiCIAiCIAiCDBsoNCAIgiAIgiAIgiAIMmyg0IAgCIIgCIIgCIIgyLCBQgOCIAiCIAiCIAiCIMMGCg0IgiAIgiAIgiAIggwbKDQgCIIgCIIgCIIgCDJsoNCAIAiCIAiCIAiCIMiwgUIDgiAIgiAIgiAIgiDDBgoNCIIgCIIgCIIgCIIMGyg0IAiCIAiCIAiCIAgybKDQgCAIgiAIgiAIgiDIsIFCA4IgCIIgCIIgCIIgwwYKDQiCIAiCIAiCIAiCDBsoNCAIgiAIgiAIgiAIMmyg0IAgCIIgCIIgCIIgyLCBQgOCIAiCIAiCIAiCIMMGCg0IgiAIgiAIgiAIggwbKDQgCIIgCIIgCIIgCDJsoNCAIAiCIAiCIAiCIMiwgUIDgiAIgiAIgiAIgiDDBgoNCIIgCIIgCIIgCIIMGyg0/DxIknTeLQgShiiKQztQ4IWwLTjffnYkSRreuzDk6dErOEMQBEEQBEGQIUM/+eSTP3cfBoTD4VCpVMFgkKZp2WoiSZIgCFEUQ/8QeIGiLqqA4vV63W63Wq0O7Uz/kCTp8/kYhpF3FgVREASapgmCEHhBEiX5KkRR9Pl8CoWCOPfrfyAnQnpFkqRgMMgwzM/dEYIgCJfLRZIkTdNNTU2nT58mCEKtVsNM4DiOpulgMMjzvNfrZVl2CDdd4AVBFGiabm9vP3HihNfrNRlNJEX+wueP0+kkSfL48eO1tbV2u53neYIglEplX/sLvPDz3lNRFEtLS+vq6gRBMBqN599fEAmSkCTpAm9EdXV1VVWVIAgqleoCLz/09SUK4kV+kSIIgiAIgiD/MfxqhAaCIBiGEUWR4zgwtziOCwaDDodDq9USBOH3+xUKBSgRF9mCUigUkiSBEACnDgaDJNmnIQdSiEKh4Hne5/OxLOt2uymKYlm2srKys7MzKiqKoiiXywVmFUmS0DhoE3I7v3BD8ZeMy+UqLS1lWVaj0fzcfSGUSiVN03V1dc8888xHH30UGRmZnp4Ok4qmaY7j9u3bR9N0bGzs0O44RVE0TQcCgTNnztx///0tLS3ZOdkmk2nYL2QYsVqtH3/88TPPPPPtt9/u3bv3k08+2b1794IFC+BhJ4hz7HO/3+/1ektKSpISk35G29hutz/22GM7duzIzc1NTEwc4FGCIDQ3Nx89elSj0Wi1Wr/fP1ixoLi4+Mknn2xoaJg+fTrLsoPv+I+QJNnY2Pj9998zDGPQG1BoQBAEQRAEQYbGL2JFdyCoVCqO4xQKhUKh2LNnT1VV1bFjx6qqqkiSlCQpJiYmOTl5/Pjxs2bN4jgOzPiL2b0PP/xw3759eXl5t95666OPPmo0GkeNGrVixYpedw5yQTWjJgji+++//+KLLxobG1euXJmZmXnkyJFHH32UIIhnn302PT2dJMnTp08/99xzcXFxd911l16vj4yMvJgX9Z+Kz+fbsGHDli1bVCrVRx999HN3p8tmjomJsVqtdrudIimGYQReoBlakqR33nln/fr148ePf/7558FrZmgIgtDS0uJwOKxWq0qlGsb+/xR8/dXXq1evjoiIiI2NZRjG4/FIkhQIBOQdSJIMBoMsy3Ic19bW9txzz506derxxx8vLCy8kFG6EDiOs9lszc3Nra2tA9kfzHie5/fv3//yyy/feOONd9xxxxBcEjo7O6urq8eOHevz+XQ63aD7HYLL5XrzzTe3bdv2+OOPjxgxgiCGweECQRAEQRAE+S/kQoWGi/kZCg4LGzdufPXVVyG8WRRFWPWtr6/fs2fPt99+++9///vxxx+Pj4+/OF0CDhw48Omnn1qt1ieeeILnebPZ/OGHH86aNWvBggUJCQm9HsJxHEEQ27Zt27p164wZMxISEpRKZVtbm9vtJgiira1t/PjxAi+MGjVq5MiRW7du1el09957L6gqF/PSBg50DCZD6N8X7ewDP51arT5+/LjT6WxsbByusxMXcL2SJImCGAwGITjC6/MGg0GwGEmS3L17t9VqraysDAaDarXa7/cPViZwOp16vZ6iKPCy8fv95133Hq7nesgj89aatxQKxVVXXfXggw8S3UMU1jFoXJIkMO/b2toaGxsvXGXoJzqp/2GBgBdJkgQhPCNG/6crLS1tbGw8duyY2+2Ojo4ebIdNJpMkSQzDwFvlQmhubj518pTb7a6uruZ5HryuLrJoiyAIgiAIgvwHcKGesbCu6Pf7A4GAwAsQJs1x3LDbwxzHVVRU3HHHHS+88AJBEImJibfddtuTTz75xRdfvP766/fee++iRYs6OjqOHj165513bt26NRAIgNEu03+XHA5H2B8CL8AK6gcffDBlypT6+np3Ny6Xy+fzEd059t566626urqlS5cmJydHRkbOnDlTqVQeO3asuLg49OwyarWa5/nW1taTJ0/6fL4xY8aYTCaSJKdPn15QUFBQUDB9+nSCIGiG9vl8CxYsiIqKWrdu3YkTJ4ZxPHtF1m5CN4qiKPBC2Mae/ykKYug1wn/2daJXXnll3Lhxff3qcrlC167ljoUS1jjMQ6/X6/P5oLdhu0nnsnjxYrPZ/Nvf/ja0EafT6ff7e/YnbD7LjYSefbg0IFilD3VZv/fee7Ozsm+66SbQF0LPIl8gx3FhRmbobizL8jwvSRLLsn6/n6Zpl8vVs7fw/MqHy0MND3XoyPQc/175cTKcm6Qw9D6GHeL1eiVJ2rx5c2lpaWpq6rXXXtu1vyDyAs/xXOg0Y2iGIAiaoseNG5eXlzdjxowJEyZ0dHSct2O9Ik+brk4KIs/zYT3sqTK0t7fDUPh8PpfLxdBMMBiU4zsAn88XDAb7Oi9DMzNnzrzssssWLFig1+tD76PACz6fT34W5J98Ph/HcfJdcLvdCoUC0nnAPvKDLEkShJgNJE+k3+9PT0+fNXtWRkbGtGnTBEGQI8Lg7D6fz+/3Q1OSJAm8EPo8DnD+w4FhWwZyYM9GAoGA1+v1+/09c52GMsDpynHcnj17Jk+evH37dqfT+YuVdBEEQRAEQX4VDEPohLw6Ch+gLMPKpiZFU8O1rK1QKJ5++umjR48qlcq5c+c+8sgj8hdwUlJSQUEBQRAzZ87829/+1tTUtHHjxtjY2KlTp0KvKIqC72+Px9NXaLr8ia/X62ELzdA0Qwu8cODAgbNnzxoMhlC3ZJ/PB9f7xhtvlJeXa7XaG2+8kSCIYDCYk5MzZsyYioqK6upq2BPyL4SeTq1WV1RUlJeXp6amjhw5kiRJURDVavVLL71EdKe1oylao9Gkp6fPnj377bff/sc//jFt2rRhGcx+6BmVLYmSIAqhuQPBaoIr8vv9JElSFEWRP95rkiQlQgLjVk4wAcMlCIJCodi9ezek25DPApk1oAX5FoiiKIkSzKJeJ1LoCjPLstAljuP4IA9d6lVJCwQC06ZNmzp1aphrgMFgAJOMoqhuE1kSJTF0Rbcf8wPEDrBOGYahaTo02eeggA7AsVOnTh07dqzBYIAeQUpUyHoICgJBEHAisCoZhgHnhbCB6meZHS6KZmgCdKXupKTQCMVQNEGfs78oSYQE+/fTJgwgQRCiJPI8T5Ik9LPXyH9Jkvx+v5JVkiRZV1en0WgSExMjIiJsNpvZbCYogmZomA8/jhJNSZJE0RRBEKtWrYJgE1AAh8CP7VMEQRAkRRICIc/hvnxAIiMjYeQFQRAEgRd4QRB4nvf7/QpGAUMEThYwHmEzGfo8ZcqU0aNHsywrHyJ3CWKsiHPrhqhUKp7n4YbSNA23DOYtz/MQfCEPcpfo0H1dfSGKIlzj3Xfffeedd8KFwymgKQhbg7kHcztsAvQvysD/EUiKpChKPhA6RlJkX0f1BUmS8H4Obb+vYz0eD8uy/aQRldm1a5coigaDQa1WY8AIgiAIgiDIhTAMQoPAC7zAK5VK8CAwGAxdC7xkl2MzMRxe9F999dWXX36ZlpZ2ww03LFu2TKFQKJVKURRdLhdBECAlzJs3LycnZ+XKlRUVFZs3b548aTLNdCWGVCgUfr9fo9EEAoFevzhle1gGPt+rqqpaW1sNBgN8ecu/gvEgiEJFRUVLS8uDDz6oUqnAwoyJiZkzZ86+ffsOHDjQ2toaExMTtj5PEITb7S4qKvJ6vfn5+bm5udBUkAuC3Qhr2rzAMzRjNpsvu+yyoqKiEydO7Ny5c+bMmRc4kv0QKiUEAgEwOFUqlUKhkH+ChV+wSWTjBMw/+VsfBooiKZImJVEC0USUREEQFIzi5MmTNpvNaDSGGfA9LTGKoqprq1UqlV6vDxUFKJLqqT5wHAeJ90VR9Hq9Wq1WJERKpCRCCquwoFQqeZ5XKpVdRmwIcDmQy5MiKbA2fT6fJEk0TTM0A2Ztr8DCqSiKcLuVSiVJkmB9wdmH9hS4XC5QGeQWWJaFG6RSqULtSZ/P53a7NRpNWKA+DJQcOiH/EbqD1J0BQaFQ0DTddUMJCfJEOJ1Oj8fDMAw8dwzD0BQNlm1f3Q518aBpmqIokJbgFoMaEqbayAY5PKRqtVqv1//oYiCKJElSzI9nDD3c5XJptVqILhnsCEuSBPY5RVEE1WUwwwiQJElTXYVgepVNQV1iGEan0zkcDoqiIPUmTFfwNJFFFpqmw14yYCpTFAW3mBf4vuQbmqFbm1opitJoNAaDQaFQwAsKJAZZa4DLd7vdXq8XUtWoVKowH5legR2CwSC4inRNlRD1UOAFjudkgYNlWRAHux6T8wHSgCiKsnsFqBVwQ4fgPuByubxer1KppCiqS9ejw7UPAIRLefrB/kT3cwH7SJJks9lOnjwZHx8vCEIgEAgEApIkabVazIiJIAiCIAgyBC5UaPD7/W1tbfCJVl9fTxBEYmJifn5+TEzM8GYufOONN2JjY+Pi4m655ZaoqCjw/aZpWi4jB8u5iYmJ8+fP/8c//rF79+7ifcXTp0+XPyW3bt2qUqny8/Ojo6N7Rh27XK5vv/2WIIjLL7/cYDCIotjY2Gi32z/88MOGhgZBELZv3x4RESHvP2PGDLVa/fU3X5eUlERERFx66aVg9UmS5HK5pkyZYrFYamtr9+7de+2114KPt0wgEKitrf3hhx8SEhJSU1NjY2MFXuAErqOjo7S0lCCIrKysxMREURR5nqcJeuLEiSkpKfX19bt27QKhASzYn2LNraampq2tra6urq2tjWEYi8WSkpJisVggMxzR7ehBEITf729sbDx16lRsbGxqampkZKRsLYBBaLVaq6qqmpubx4wZY7FYoIxCWXnZq6++6vP5bDbb559/Lp83KysrIiICGiFJsqmpqaqqqqWl5cyZM1qtNjIyUnZzANLT06Ojo6OiomAQfD5fU1OT1WrlOK60tNThcJjN5tzc3JiYmJiYGEqkSLorugH+XV5eXlZWZrFYIEpFvi8HDhzw+/3Z2dmJiYkdHR3Q/6qqKpPJZLFYjEaj7FQStoIaDAbr6+tPnz5dX1/vdDp1Op3RaNRqtaG5/QoLC4fwUFRUVLS1tSUlJUEpClgDr6+vb2tr0+v1ueNy7Q57a2urw+E4evQoKDiZmZnZ2dkRERFg7kKVFkmSwB4Det53qKyZnZ2dnJQsbz979mxDQ0NFRUVjY6MgCJB11WKxjBo1ChSogZhhkijt2bvH4XC0tbXZ7faRI0cmJSUlJSVRFAUDAt5PLpertrbW7XbX1taazeaysrJNmzYJgqDX6xMTE9PS0vpZlP7hhx+cTmdmZubo0aMH+1yUlZVVVVXBy0EWnkiKVFAKeQ5kZGRERERERUaB8iKfoqioSJIkeEwUCgW41YCs2dbWVltb29bW1tLSAvPHZDJNmjSJ6HazgnQbHMedOnXqxIkTEydOzMjI6Nk9+VloamriOM5kMiUkJIwePdpsNkdGRnbpLxQFao7NZuvo6Dhz5kxjYyPHcRaLJTk5Ge7a+W+TJLEsW1xc7PV6c3NzjUajQtn1npTf80eOHAkEAhArl5aWlpiYCPcxKiqq/5ZJkmxoaHA4HK2trXa7/cyZM/KYXH755YO6Xw0NDfKcZFkW2jGbzZmZmWazueeELCsrk99p2dnZMPdomoaKM3Avjhw5UlpaeuLEiZSUlO+++85ut4N30ujRo2NiYvpKtYMgCIIgCIL0xRCFBvlT+/jx4+vXr9+xY0domcCYmJhly5b95je/0ev0/ftXnxf4Cty9e/fp06dpmr722mvhizbM7CRCIjhmzpxZVFS0bdu2AwcO5Ofn63Q6SID30ksvud3uZcuWPfDAA0QPQ7Gjo+Ppp5/WarUWiyUzM9NkMlVWVj7//PP19fXBYNBgMPztb3+TJKm9vT0yMnLs2LHz588PBoNHjhxpb2+/9NJLR4wYAa3xPK/X62NiYhYsWLBhw4ajR49ee+21sJQXerq6urqamprRo0fDRzZFUypG5ff7n3rqKb/f//bbbycmJoY6wOfm5paXl3/88cfLly8fNWrUhQxpT5xOp8Fg8Pl8e/fu3b59e1FREUSAR0RE2O12giB+85vf3HDDDWPHjiVCvPp5nocEGdOnT3/jjTeIHln0NBrNmjVriouLr7nmmr/+9a8cx7399tvr1q0DuysiIuLJJ5+EBVKe5++8886VK1fCwmxDQ8N77723bds2iqK0Wq1Go/F6vY2NjR6Px+v1jh07lmGYjIyMp59+Ws5UV19fv3///g8++KCsrCwpKcnnIk+S8AAAIABJREFU84Ebgslk+tOf/jRnzhwl86ONyvP8559/vmHDhvj4+NBoFIfD8eWXX77xxhubN2/2eDwvvvjisWPHfD5fVFSUw+EQBCE+Pv7qq6++5pprIiMjQ7Uqu92+fv36DRs2dHZ25uTkaDQaq9Xa0dEBwgfLstHR0WlpaXPmzBnUfYHJv379+p07d15yySVvvfkWQRAUTXEcV11d/fjjj0+YMGHlypUlR0v+ufaf9fX1EP4TCAQ4jps9e/by5cvz8/Mh6QPRI88FQRAcx1EkRTN0WVnZk08++f3338+aNSszM5Pozg/y73//e+vWrTU1NbB0LIqiz+cDdW/hwoX333+/IAj9CA3gIFBcXPzxxx8fPHiwvb0dDLaampqIiIjZs2dfddVVkydPhu7xPF9bW/vCCy9UVlY6HA61Wl1ZWfnCCy+0tbWZzearr7765ptv7mvmOxyO1159reRYycsvv5yenj6oQSYIYvfu3S+++CLLslu3btXr9XLogSiKFRUVjzzySGlpaXZ29uuvv242m2mCDp11DzzwAMdxq1atGjNmjM/ns9vtgUDA6XTu3Llz9erV9fX1SqUyMjLS4XAEAoHY2Nh77713zpw58PiA3CNJ0q5du1555ZVbbrnlD3/4Q4Q5guM5juP0er3ACwcOHti6devGjRslSdJoNDAlFAqFwWDYvXs3QRDgIEDTNMuyLpdr/fr1X331ldfrValUgiDYbLbY2NjCwsKbbropJSWl52szFJIkoerEvn37HnvssWXLlsk/lZeXr1u3rqioKBgMQiMul4uiKKVSmZmZ+de//jW0EfhDOjetic/ne//99/fu3VtbWxsMBs1mM8/zDofDZDLNmzfvxhtvhNdLXwSDQfCrKi4ufv/99w8fPux2u2NiYtRqdUdHh9vtZhhm0aJFTzzxhJzKhCTJmpqaTZs2ffXVVydPngTdze12R0ZGTps27a677oqMjNTpdIIgFBUVvfjii/X19SzL2u32DRs2fPzxxyRJOhyOhQsXPvfcc4OdUQiCIAiCIMjghAaBFwRRkB3pv//++//7v//r6OiIjY11Op3ybpWVla+88kp7e/udd96pYTR9t3ceumK8KaKqqkqj0RiNxlmzZvWfdV+SJFjR3bVrV3t7u9/v12q1XUkEKAq+7OHfsIjaayM6nU4UxISEhFtvvfXw4cPfffed0+m86667LBaLy+XS6XRJSUkEQbS3t7e1tREEkZ+fr9frKfLH1rRabX5+/rp16+rr66uqquLi4kI9ugVB2LFjB8dx+fn5FouF6NupHroq8EJeXt4HH3yg1WpLS0shp8MwujOA5bBz585XX321sbFxypQpM2bMUKlUsHT57bffbty48eTJkw899JC8/i/w/VmYxLkB22BJMgwzevTolStX7t69u6SkxOl0Pv7446AyiKKYlpYGPikdHR3PPffckSNHkpKSlixZkpycrNVqq6ur9+zZU1xczLLsLbfckpCQAC4Dfr9foVD4fL5nnnnm4MGDERER119/fW5ublRUVEtLy759+06fPv3ss8+eOnXq3nvvhZkAAQ5yEEFonzUaDUVRycnJn3zySUdHhyAI11xzzdixYyGxaEVFRXFx8VtvvaVSqW695VZ5HIJc8J///Oc///lPi8Vy7z33po1O02g0Tqfziy++OHTokM1mW7x48ZQpU0wm03lrPfQPOKjLGQFomu60dq5Zs+bQoUNTp0694447CIJoa2uTJGnHjh2fffZZIBBYuXLlyJEj4XA4SvaxlySJIimP19PU1PTaa6+dOnVqwYIFjz/+eExMjMALHMcdPnx448aNbW1tM2bMyM7OhtIGLpersrKyvLzcYDCEJRToCUhXr732Wmlp6WWXXTZ+/PioqCjITnLw4MFvvvmmoqJi2bJlS5YsoRmaJmhBEObPn+90Or/77rtjx45lZmYuXrxYkiSaotNGp/10pWQmT548atSoxsbGzs5O2XOHIAiKopqbm202W3x8PM/zoAzKgylJ0smTJ10uV35+fnZ2NgQdGI1Gu92+f//+w4cPp6enL1myxGg0UhRlt9tPnDhRVFT097//PTIycuLEiVCYUxRFCM0IRaVSqVQqv99/8uTJF154obGxsbCwMCMjA9LEeL3es2fP6nQ6sL3By4Mkydra2ieeeKKtre3KK69MSEiIiIhobm5uaWnZvn37tm3bRFF8/vnnA4FAX/EFPWEYBp4Uh8OxevXq/fv3Z2Vl/eY3v4HAHLfbbbfba2pq3G63/BLr+VICuQHiUKqrq6OiosaPH5+SkgIBZR6P51//+teWLVuqq6vXrVvn8/kMBkOvnQkEAgqF4tSpU6tXr66oqJg2bdqYMWPi4uL8fr/H46mpqamsrIQAOnhxKZXKM2fOrF27duvWrcnJyQ899NCIESNUKlVdXV1xcfH69esrKiqefPLJ7OxshUIxcuTIe+65p7m5+fnnn9fr9UuXLgWHMp/Pl52drVKp+kkAgSAIgiAIgvTK4IQGmqE9Tg/YS3V1dWvWrDl9+rTJZJJzqsPXGOQqW7t27YwZM/Lz84fcOY7jSJKkCbqkpISmabPZHBsbG1ZLIgxYcBs9erRGo4E6EVAujmEY8BsfyHkhhfuIESPi4uISExNPnTrl9/vv+/19kNMOFg9hBGprawmCGD9+PMuyoaa1RqPJz8/PyMg4fvz44cOHr7vuOiLEh6Kuru7YsWNGo9FgMBiNRtjeM4/Dj+PAc9nZ2dHR0S6Xa//+/XPnzu2Z7e9CgJoCa9asqa6uvuWWW66//vqMjAye52mKphm6sLDw/fff//jjjzds2JCenq7RaPpaFw3rEtkNCEPBYHDWrFkEQfh8viNHjpAkCTUFIEJejgb/4Ycfjh8/HgwGH3vssczMTHDOHzNmzLRp0/7yl798+eWXERERsmsAq2D9fv+GDRsOHjyYnJx8//33z5w5U86zcN111+3atevpp5/esmVLQUHBlClTlEolx3EMzYDqFDYfQHfQ6/U7d+6cNm3abbfdlp+fz/O8TqcTRbGpqYkkyQ0bNnz11VczZsxISUkhSZIX+Kamps8++4xhmKVLl1675FpIMaDX6wsKClauXLljx47Ozs7JkyeHuvwMyy1TKBSNTY1nKs48/fTT48ePj42NhdwNkiSBfw2E7fTqjQ/QDN3U1PTkk09WVlbOmTPn4YcfjomJIbof8+Li4pKSkrvuumvFihXR0dEQ3E4QhN1ub2pqGjlyZJALBtwBjUbTl4DidDo3b95cU1Mze/bs3/72txMnTvT7/ZIkLVy4sKGh4d133127du0nn3ySm5sLBnxmZmZeXh6Y34cOHYqLi1u6dCkkCxxIgMaQn4jMzMzMzMyGhoYDBw6EFkMJBoN79+5Vq9W5ubnFxcVlZWVXXnklQRAUSUEVDCgrk5mZCYMMoQeiKH7zzTeLFi1atGhRVlYW5GXgOK6srKyjo6OkpGT79u25ubkU2Z0/lfrxSYG8CHB2lmVfffXV6urqyZMn33HHHZmZmQaDAcbh7NmzsIPP51Or1ZCIobW11ev1rlixYt68efDEKRQKt9sdFRX1/vvvnz59ev/+/VOmTBnC+FRVVX399dcpKSmPPfZYVlYWSBtg+ZeXl0dFRbEsOxBT/P7771coFCNGjACpAnwxgsHgm2++2dLScuTIkcmTJ/d1rF6vDwaDVVVVu3btWrJkyX333ZeSkgLdUCqVgUDAarWqVCqtViuKInjfFBUV7dixIzo6+ne/+93cuXOJ7nRCV1xxxcsvv7xz5869e/fGxcVFRkbGxcWNHDmysbHxxRdfJEnykksuueLyKziek7VIURAv0DUPQRAEQRDkv41Bp7kCI9Pn8x06dOjMmTMQYg1mm/ypbDAYtFotTdMbNmy4kM7JZcnkou4cx523qLvH49FqtWq1GjwaYKMUWntR7Kon0FcLcBZIqufxeHied7lcNEMrFAq1Wg3mHEEQbW1t4NEA383kuQUOtVrtpZdeGggEjh8/brVaeZ6XjaVt27ZZrdaRI0fOnz+f6F70UyqVcl43WbOAvGsKRqFWq0eNGsXzfFlpmXyWkEu6oEpsDM28//77Vqs1JyfnxhtvzMzMdLvdgiDQDM1xXGpq6qJFi7Kzs7/99tuib4sgHztFg48IJd/0ftqH4QL7BLoKmQVD94FzkSRZUlLi8Xjy8/PH5IyBD30oqmc2m8EK/frrryGCGo7q7Oz88MMPIyIirr322hkzZoDNI/ACpIS46qqr5s6d63A41q1bB4MpCILc+TDzFe41VDBdunRpQUGBSqXS6XQul0sQhBEjRsycOdNgMFit1ubmZrhkpVJZWVnZ3Nyck5OzePFiiNKXq2yMGzfOaDQWFxefd9IOFkEQGIaxWq0LFiyYM2dOTEwMnALSMcycORNSFdTU1Fit1tADQ+/UmTNnXnvttYqKioKCgrvvvttoNEJxUMiZ53A49Hp9enq6xWIRha4JCSvG2dnZarWaVbAmkwnWvUNPIc/er7/+es+ePVqt9g9/+APkCFCpVGq12ufzjRgx4rrrrps2bdp3331XVFQERTTloiF2ux3cpiiKCgQDEMUA+flCTxQ2/+HS5FKLA38uOI6Li4vT6XRHjx4NHSWbzXb48OHMzMxx48YlJyfv3LkTavcKokDRlCiKZWVler3ebDZDRYZAIAA9NxqNt99+O0xXeTJkZmZOmjRJpVIdP35cFEVe4Pvv1QcffFBVVRUbG7ts2bLc3FzIeghXlJSUFBMTAxfo9/t5nuc4rrOz8/rrr1+yZIlCoZAfLp1Ot2DBAo1G09LS0tDQMLQXhdVq9fv9I0aMGDVqlJxuBtS61NTUAWbfJEly9OjRo9NG63Q6gRegWg3LsjfffDO895qbm/tvwePx2O12lUplsVgSEhIg/EStVoN3z4gRI6C6J7wx2tvbDx065PF4Vq5cefnll4emaM3Ozp41a5bZbP7iiy88Hg9kfgXJgyAIURQ9Hg/N0CqVCl5Zoih2VSTBgpcIgiAIgiADZtBCg2xfnT17FgwSWWKAbGQkSULldq1Wu2vXrtCQikF3rnvd3mazQYT8eV0SKIrS6XQWi4WiqPr6ernquyAKsse4eI7q0DvQmiRJ8MkOKeVDN4qi6HA4HA5HYmKiXAsw9MDoqOjMzEyGYfbu3Wu1WsHeIEkyGAwWFRUZDIbExETwtgiNau61Jx3WDvgoJ0nybP3ZnlctCmIwGISimF1aSjcul+u8n8g2u62mpqa5uXnKlCnp6ekgFanVarhShmEmTZpUWFjIcdyBgwdAu4HbDdUKZaGB7AM4u2wNysEvYLYRBCHb4QzDNDY2QvwCzdA0RXMcp1arVSqV1+tVq9Umk6mlpYUgCJZlwXLYv39/U1NTVFTUJZdcwipYOAsExWi1WkmSbrzxRr/f39TUtHfvXp/Pp9FoSJKUPVxC+wkihSiKEyZMiIuLk4eRZVmIixkxYkROTk51dTXMaq/XSxBETU0NwzANDQ0mk8nv9weDQcjwR1FUQkIC5Oaor68PreV54UC+UrVavXjxYo1GAxUu4NYrFAqtVpuSksLzfHV1texuIN8mQRAkSQoEAi+88MKePXvGjBmzatWqtLQ0UAHApgLLTavVvvfeewcOHCC61SI4FkotQnXJnjITVE7x+/3V1dWiKF555ZWJiYlRUVHylGYVLEEQWVlZkAG0tLTU4/EEuSDoPuBpIleEhcQERHf50rAaijJanRa6EVr/khiYGKfT6XJycsxm8+7du202m3xFx48fr6iooGl6wYIFCQkJlZWVR48eledqbW1tSUlJWlra1KlTYXAkSRIEwev1TpgwAaKrdDqd1C0jKhSKjIwMk8lUVlYmCIJSqSSp0Nl3TjUQURR37tzp9/tHjhwJcUwwOGR3VReFQiEKIsMwCkYBNXGys7PHjBlDkqRKpSJJEoQ2SZKMRmN2drbdbq+vr+d5PrRMZk96ff+kpKRoNJqGhoYPPviApEgopQEr/CzLhpU4kQmdbyTVVWcEfgIzXhAEcMwxmUxer7esrKz/jpnN5pSUFIPBUFJSsmPHDrl9UPcIgjCbzSANqNXq77//vra2Ni4uDpKAQDoJmqHhmYXXb1NTU3t7O7QPmgX870yr1RLd5XXkCizEkEpjIAiCIAiC/NcyxMJdHMdVVVXBEjHYY0R3ETvZ6oBF1wvpHE3RsF4KfhNOpxO+F/s5hGEYURBtNhvHcVBoM2wHOT17z9DoQSEIgs/nE0URKi/KJfEokpIkCVaA09LS4uLibDZbVVWV3W4HU7Oqqqq8vFyv10+fPh2uK0xoAOM29FsfzHKdTkfTtPxr6D4kRbIsy/Hdy6eCKIkS/EPTNCzZ9fOV7HQ6IbVbXFxcX/vodDq9Xt/S0hJm6Z3XiiO6M02EEXaIwAsMw4AE5HQ6W1tbwYuEYRhIcQ+JDwKBQFJSUiAQEEWRoRlJkjo7OwmC6FnTUR7YyMjIhIQEn8/ndDp79iR0nEGAEAQhMjJyIOUhOI4TeCEqKkqpVOr1+qamJnlewdX5fD6YGyNHjhxGpwayO9MEQRAWi2VQ9o8kSSzLOp3ON954Y9++fXl5eatWrQqLhQG1buHChcnJyc3NzX/84x/ffe/d1tZWSZKgtiVkK5Q70+uJ7HY7ZEA0m82gLMjIXujJScng8+/z+cBtgSAIKNkIRntPq/gcOa97kofWEA2TEQcyJhzHTZgwwWQyKZXKU6dOQTter3fv3r06nW7cuHFQHIFl2ZMnTxIEQVO0JEknTpyAEo9g3hPdJXJ5nj/v5AkreiqrDPL7E9JDKJVKOcVGWOWarpGE0puCQBCEUqnsy+YPY7DjEx0dff3111ut1rVr1958880//PCDx+ORfw0EAq2trX1NA4qiwJkL5ABIZRoIBFwul9VqbWxsrKmpCR2NfvpMEERCQsKiRYvOnDnz0ksvPfTQQ1AnBSK/wqZKZWVlbW2txWKpqakpLS2tqKg4ffr0mTNnrFarzWaDcW5vb6+rqyO6NSyI0uoZB0RSpCyoodaAIAiCIAgyQAYtBMByNEMzsCQV9issdMPfYIRfSOegcH0wGIyLi6upqZFXn/qBJEle4Nva2oLB4IgRI+RlNJrqWiaFfxPdi959NSKdL+QYPGw5jouIiFCr1ZC0suunbj/z/Pz8/Pz88vLyLVu2TJgwQa/TEwSxZcsWiqKio6Pz8/NtNptcTo8IMTlkewO26PV6UFsYhgEbm+d5WNWUj4WFdLfbvWbNmsrKytB+ut3u5cuXz5gxo69r6ejosNlsCoUCwp57JcIcERERUVdXB+vVYAfKNSN6zgTo/MBzSQhi1yL5lClTzp49e/DgQSjfCCu0bre7paXl8OHDjY2NeXl5Wq2W4zhIuAAe1yaTSS50GookSfATpPqTrQg5J6J8s6DDEBeTkJAwEKEBzjhp0iS/33/27Nldu3YtWrRIzhBBU/TXX39ttVovu+wyWLkdSKKBgRBqJZrN5kAgAFLUQAwhkiRdLtc333zz6aefBoPB6dOnWyyWsOyqFElZrdakpKRVq1b95S9/KS8vf+edd9auXTtp0qSrrrqqsLAQwuCJPoxDhmEEXmhtbe3o6PD5fDExMaAs9OxbXHycXq8HD5GoqCioYQFCA9wgQRQoaaCDNuThpShqxIgRiYmJx44d27NnzxVXXEEQREdHx549e+Lj4yG/xv9n78zDo6iyv19LV/W+Jd2dkI0QkhCyQFgDwy4ICMgyijqO4DLqC6ioOKioDMg4KP6EAQEH1BFH0GFXUUCQRYSQAElkC1khZN97S6e7q2t7/zikbDsLASIucz+Pj0/srq66de+t8p5zz/me1NTUjIyMb775ZubMmXq93ufzHTt2TKFQSKUcIHSFklEwfzpzXan3WqcggV8Mx3GIjMAwDMpqtnkeaR8+4BHAW8UZETghaUB0Hp7nX3jhBYVCsX379tzc3GeeeSYiIuLBBx+85557REGUy+VBQUFQDqPNtkH8F47jFRUV58+f/+677y5cuOB2u91utyiKBEFA8sV1XxSgm/Pggw+63e6MjIwjR44cOXIkLS1t3Lhxo0aNUqlU/sLDoJNSVVX15JNP0jQtk8lYliVJ0uv1qlQqu92u0WjgD6nbZS202Yc32mkIBAKBQCAQ/+Pc8NIcMmBJGZmamgqftA5mBncDwzBBQUG33kSwsXEcdzqdHo/nuscLgmC326HMZAcLxM6vHdvckMdx3O12C4IASoGwfAdrWYIgiEGDBiUmJmZlZTmdTlJGulyub775Jjg4GGJ3O7kDCbnZGo0GzDDo8IBuh7B/iqIEQcj24/z584WFhVLeR5tARIMoilJEw7VL+BnhGq1Gq9VCUQNpf08y19t0NADXzXYB8wkiMnAcv+eee+Lj42mafuyxx7777ruioqL6+vqsrKw1a9acOHFi/PjxY8aM0Wg0kLYjCAJENKjVao1GE9D/kL/gcrkIgvB6vVIWz49eBkEQxB//ga4G5xHmt2Eutih6+EeRwKncbndUVNSf/vQnlmU3bNiwZcsWQRAaGhsuX7688MWFJ0+e1Gq1S5Ys6UIvg3+nYRgGyRqYn6BJx+A4XlVVtWvXLrfbTRDE5s2bz5w5I4m5AqSMDA4O5jk+JSVl06ZNb731VmRkJMMwWVlZzz333OOPP15QUIC1/wQRBCGI1xKLGIYxGAxtHubz+XielyIapMAEGBbwXoEb60Y65sc2dP5gyDLo0aNHdHQ0xCyIotjU1FRRUQEygSAaEhwcnJ6eDkKMgiBcuHABEv6h9675BwlcFMVbf+9VVlbK5XKIRcJaslHaA8x40BO5xeu2SVBQkNlsXrp06bZt26AcbHl5+Wuvvda/f/9du3dBhEJ7He5yuSiKAtfAxIkTX3vttRMnTni93ujo6L59+44ZM2bq1Knw9uh4yHAch1MlJia+9dZbS5Ys6d27t1arzcjIWLRo0RNPPAHFPiXgWXC73d2jugcFBRmNRqPRqNPpzGazXq/v0aNHcHBwbGysSqmS3LUcx/kr6QiigPwLCAQCgUAgEDfNDUc0gEibKIoTJkzYtm2b1Wp1OBwKhcJ/dx1WhKIo/vnPf26vXFlnkFJwR40adfLkSY/Hk5ubO3DgwPaOF0URFARABjIpKQm2FkF6jed5tVoNG86t99/sdjuY8XK5HBLyYakKi/iGhgb/HHtREEmCDAkJ4XnearXCfixs8gdsiCUlJclksrq6upzsnJ4xPfPz8+vq6iIiIkC+3uv1dia7hGEYmqZBbwJaTuCESARalTAuiYmJf/rTn6QPIXQZdqH9t0z9iYqK0qg1Pp+vpKSkR48eJEnC3jLkkINFWlJSUlNTExoaSpIkOBooivL5fFBmsk0Tt6mpqbCwEPYzpf7EW6QEIIYfevXaCWUUhmE6ne7dd9/961//evjw4ZdffplhGJlMBoHNqampzz33XGRkZF1dnVKplMI6INADytr5NwAkBiH0RqFQQEkF7KfKoG0mU4Anwr9eaZvAyb1e78KFC00m03vvvbdly5Z///vfGIZZrdbg4ODU1NRHH33UaDB2rZcBa/EEGQwGSBHq/A9hFFJTUydPnvztt98eOXLk1Vdf3bp1a1RUVMBDAWEIOI5PnDhx9OjRZ8+e3b59e3Z2dlFR0csvv/zYY49NmjQJx/EA4xaGksCJiIgIyI3HMIzneCldAmag0+mEuqSCIISGhhoMBkEUIDsgQPtDGgVp/kh/S3quMJSSG+hayBJBSF+1N/OlziQIIiEh4dNPP71y5YrNZtNpdf/9738VCkV8fHxoaCiGYb3ie4WGhgYFBR0/fjw+Pr6kpKShoSEyMnLw4MFSmAxsm3dJjkxsbGxzczNJktLtQ2Hdn3RFS2wC3CPYyf5PYlfZyZJnLSkpaf369UVFRTt27Pj+++8rKyuXL19+6dKlpUuXtvdbrVbLc/zFixefffZZpVKZlpb24osvgjIovK6VSuX+/fux68WR4TguBX/RND1x4sSJEydeuHDhq6++ysjIKC8vX7RoUU5OzhNPPAG1Nj0eD8MwY8aMWbt2rX+uDeaXuROA9HaFwJDOJHQgEAgEAoFAINrjhh0NkMfOcZzRaJwyZcq+ffukIFjpGJZlVSrVwIEDx4wZc+tNBG3z5uZmgiCOHTvWgaNB4AWSJM+fP3/06FEoWgbZCpDyrdFobDab3W7H2lo+VldXQ351gHplU1MTx3F6vV6qKi99pVar5XK5w+Hw+XyiIBIyIsBqdblcCQkJgwYNunz58reHvn3gTw/s37+fYRiNRjNs2LDWRlp7gAEGpSuCgoJkMhlO4LSM9u/zxsZGjUajUCiGDRsGxSwkRFGE3WPQPmx9frlcTtGUz+drbm5mGEapUIJ5A2nzUJcRRPWjoqJwHIc8ERzH1Wq13W5vL9k7ODjYarWCrgSMAkRuS7HxWIsMpNvtVqlUkgHAMMyQIUPS09MjIyPDwsLMZnNUVJTZbJ44cSLkYysUCq1WKwiC1+vt2bPn5s2b3W43hEP7N4CSUZAg09jYCCoA18xgv6oTPzGtW5JrAiKoCeHayMIB/r9yOp0gORkbGxsaGhoeHt6tWzeFQqHX6wcNGvSHP/zhZyqM17rDO2kRiaIYERExf/78Hj16GAyGS5cuFRcX//Of//zHP/4BEewBkxw8d0qlcsiQIf379y8oKHj77bdPnToF2UCxsbE+n88/rf2axABJ6PV6eGpKSkrYEWxAJyiVSpIkQa4SCoX6C4JKepAEQYDk5A11TlNTk1wuh1Z5vV6pHm17wGgOGDAASreeP39+8ODBp06dkslkY8eOhdgZmqaHDx+el5d38ODBefPmnT9/Hsdxg8EQERGBtdRNhBifW3cqQZ+DE62wqBDDMIqiOnB7gePsFi/aSeAlsGjRotmzZ69evfrIkSNffvnl6NGjO8jM4gX+o48+4jiuf/+nG81nAAAgAElEQVT+69atk2aXJAp70yZ9cnJySkpKYWHh22+/fe7cudOnT6elpd1xxx0EQcTFxSkUivLy8sbGxk4GmMB0E0UR3mnIxYBAIBAIBAJxK9zMmhh8DRiGzZkzZ8KECXFxcaAMLxEVFTV48OAXXngBbNRbJzY2dsqUKYIg7N2798KFC1LRytZYrda9e/deunQpLS1t+PDhYN5AALZer3e5XHV1dVBGEftp9nJmZibshYJtI90pVJXTaDQNDQ3+F+J4LigoSK1W19TUeDwelmMxDAuwZ8DuTUlJCQ8PLy8vt9lsly5dslgs/fr1g3hy/+19ad81AKwlPbuqqorjuNDQUJqmIdjY/7Dg4GBIm1er1AFWxzXbD/9xgzfgAL1e37NnT5qmT5065XK5cAKXegAiDlwu19WrV0VRHDJkCE3T0B4cx6G6B9SDbD0WRUVFPp9PEsUgSRIsfMmMBAV4rEXNTuKLL75Yvnz5qFGjtm/fvmrVqldeeWXWrFkTJkyAHobiCPAriHbR6XTV1dVnzpwJMMZIGenxeNLT0202GyTh+yeD3DoESUCoxdmzZ+fPn28ymVauXPnmm28uWbJkzpw5KSkpkGTUhVcEpN37m/gtmPFGo9Htdg8eNPjRRx+NjY09ePDgBx980NoHJ4qiWq0mCAKU9miaTkpKWrZsmclkunLlytWrV7FWcx6A/efk5GS9Xp+ZmQnpBv5QFNXY2Hju3DmGYVJSUvxLJEquHJIkSaJt11jHgG8LpDqhHi3mty3fHkajsU+fPiRJHj58OC8vr7S0NDo6esiQIVD4EGqRhIaGXrp0qays7PDhwyqVKikpCQQaXM2u9sph3ATg4Bs6dKgoipcuXYIauuBwue5d+L9P4BPcr54FxCjdStvkcjmUODGZTIsXL7ZYLCqVCuqStAdN0zk5ORiGpaamwosCIo94nsdx/Ny5c/6xKp2EZVmHw2G1WlmWjY+PX7dundlsvnLlSnFxMaSZhIeHq9Xq0tJSEIzs/K0xDFNXV8dx3C0qGSMQCAQCgUD8j3OTm2+Sr2Hu3Lnz58+fPHnyQD+mTZv297//3WQydeGm0NNPPx0WFubz+datW5ednd2mr6GyqnLfvn2ZmZnx8fHTpk1LTEwURZFlWcheDgsLUygUdrv92gb7T1XKMzIyoEZAgAqgyWSC2nJlZWX+S3xIxJAiGjowM4YMGWI0Gq1W665du0pLSz0ez8SJE8ESkCzt6yIKIqyq28t4/8nBPzVFrmsC6XS6lOQUpVJ56NAhm83mcrlAcpJhGAimOHHiRHZ2tkwmGzZsmKQ7iGFYUFCQwWCw2WwVFRWtL/rRRx+pVCoIEMD8ipWCo0Emk509exYMj4A1/ebNm0mSHD58OIgscBzndrtdLpdUrJSiqKamJtgRjY2NTUxMrK2tTU9PD9BoEATB6XTu2rWLZdl+/fr17t0bPmdZluM4qNLn7yCDz+Er7qeAK0T6FexyYxgGrrQDBw4YDIaEhITg4ODGxkYMwxQKhU6nk9Pyn2mr+aYjGliWFQXR5XLp9XpSRt5///0TJ040mUwffvhhZmZmgFgDjuM0TUNdQAzDvF4vQRAgTQppER1of+A43rdv3+Dg4HPnzhUWFgZ8y3N8aWnp8ePHQ0NDBwwYAC45uCm+BY7jOJ67CQNepVJRMsrhcFRWVR4/fvzSpUug+Ye142iDV0FTU9Po0aM9Hk9WVlZOTo5er09KSpLL5TJSBn6xqKgojUajVqt/+OGH7OxsgiBGjRoF0wbDMH9fyS0C3qt77rnH6/XW1NQcPHjQ6/W2Lo8qCiKO4wHyDR2opdwoIFDi/4k0x0RRpGSUUqns3r07x3EdTAOv1wv6iyEhISzLgl8GsmZAhGL79u2S27TzbZPJZFqtVqfTgTvJ5/OpVCqIuoKLpqSkJCQkYBj25ZdfduaEgiBQFAVBbfD/CKkMym2LFkEgEAgEAoH4PXHzmzZ4Sxm2ESNGjBgxoqamxmazYRhmNBqDg4O7sJgfYNAb7r333g0bNhw9ehTDsKqqqsTERIvFYjab7XZ7XV2dzWbbt2/fsWPHfD7fuHHjJk+eDAtESJwmcOKuu+46c+ZMXl7eJ598MmnSpLCwMAzDHA5Hbm7uzp07wcvgdrvr6uqSk5Ol63br1g3Kqu/cubNPnz6wwQsJBUajsX///vv27btw4UK/fv1atxm0FUJDQ9PS0srKyj799FOXy5WWlhYfHy+KoiiIAcZJa/F52KJ3Op0NDQ01NTUajWbq1KmiKHaw29Z6Cx1qtnUQDMxx3Jg7xpxIP5GTk7N06dJHHnlkxIgREB9RU1uTm5v7wfsfuFyuKVOmpKWlQXkL+GGvXr3Cw8Pr6uq2b98eEhICqddNTU11dXWQ/w//GXDd1NRUMJD27t07aNCg+vp6s9kMSh8yUsYLfEpKSkNDwxdffAH5IHhLJQ5RFBMTE2NjY3Ec12q1kOEviuKCBQuef/753bt30zR933336fV6KEZYXV39ySefnD9/vnfv3pMmTTKbzT6fj8RIUKEnCCKgACqO4yRJarXaAHtbpVLxHA8d2NzcrFarpTHyer2UjIqOjrbb7WfOnNm2bZtCofB3BpnN5sTExI5T9wmCwIhrG7wQMA/SDwqFAqJvwNvCcRychGEYOEbSIwACHGGQL6PT6UAiQUbK4K41Wg20EIqePPfcczBekD0BMfCCIGRkZLhcrpiYmPDwcCk0qaamZvfu3T6fr0+fPr169eogZAnH8cGDB48dO/bLL7/cuHGjWq2OioqKioqiKKqioqKqqmrjxo2FhYXjxo0bPXo02Jw0TXs8HplM5vF4OI7DcbzNmo7XRRRFKFjzr3/96/jx4/369Zs7d27AWPv3FcTvaLXatLS0hISEq1evZmZm0jQNMQUESfBevr6+Xq/XDxgw4MKFC3v37hVFMSI8YtCgQXAGSOQBTcQAwRoYUxg7tVoN8qWCIDgcDhA34TiOYRi9Xg/aKPAriqJGjhw5cODArKysbdu2sSw7efLk4KBgUkbabLaysrKKiorx48eTOMnzPEVT4G6AZ0S6LnjxwFqWy+VQHpjn+PYKT/h8PrVKTZJkc3Ozv38nNzf38uXLffv2DQkJkUbcx/pOnTp15coVt9vdQYocx3EajSYhIeH48ePff/99amrqwIED4QVy9erVY8eOHThwwGg01tfXezwekGxo71Qej6egoKCysrJnz57h4eGSzwLDsH179xUWFnbr1i0tLY0kSIh7+uMf/1hcXHz06NEFCxY89NBD3bp1gwpBPMfX1tVWVVXV1NTceeedUv0UGSmLioq6dOnS4cOHZ8yYAfo+4LmQkTJBFEA6pL3mIRAIBAKBQCD86bLo0NDQULPJjP3U0O3CTFdSRk6aNEmn03311VdHjx69cOFCz549zWazRqMBO9zpdBYUFERHRz/22GPDhg3z/y0sRgcPHhwfH5+enr5ly5aCggKVSgVh/1evXhUF8Y033nj11VdJkgxY6Xbv3r1///7l5eWZmZn//Oc/IyIiampqoqOjH3300Z49e2q1WoqiCgoKmpubwepr057s37//gQMHIDR93LhxarUaZCP9le06QKfTff311zabzWazxcfHd/lil6Zpi8Xy7LPPvvfee4cPH3733Xf37dtnsVigfmdBQUFFRUVaWtoDDzxA07S/ZpvBYJg2bdratWuPHj1qtVpDQkKg2mJFRcXp06enTZvm9Xq//fZbMJ/gVzzHp6am9uvXLycnJyMjY+nSpcHBwaA98fjjj2MYJrDC9OnTs7KyioqKzp49azabsRZxBEpG0XI6LS1t/PjxiYmJUN4STLJHHnnks88+O3DgwOXLl8PCwtRqtcvlKi8vLywstFgsjz/++MiRI7GWOH8pkMFms/l3JgwfhCS0TomHPBqLxXLu3DlpkkAY+cSJEzMzM7/99tv169fjOO6vzqhWq2NjY++5557k5OQOfA0Oh+OazCdBQLQOTE6NRnOt33gePBEYhul0OpIkPR4PFOPEWupo+EuEgAgCwzBNTU1gkpEy0m63kyRZX18Pxi2oS+A4/uijj549e7asrGzbtm0DBgxQq9UNDQ379u07cuRIcnJycHCwyWSCzd78/Pzy8vKgoKCZM2eCJdbBM06S5KxZs6xW6+eff/7WW2+BOCvLso2Njbm5uSUlJWlpaQ8//HCIJQQUNBiGAZkDcDzJZDJSRnq93o4VFloDuhgej6eysvLq1auTJ0+2WCySIoA0mv4/gbsIDQ0VBdHr9ebl5cnl8uHDh8OMhZomHo8nLS3t22+/PXPmjEqlCgkNgZQxqdshyoNhmACdF0mBoqmpSaVSQW0XvV4Ps5eiKKVS6XA4SJKUy+Ug2trU1KTT6Z5//vm//e1vJSUlmzZtOn36dFBQEI7jDMM0Nzc7nc5x48ZhGCaTyVQqFcMwKpXK7Xb7y5TA3w0NDSqViud58BE4m5xtFoKF9jc3N4NQBUVR0ps8Pz//lVdeGTdunEwm6969u1KpbGpqKi8vz8vLYxhmxowZ48ePb28slAolhmELFiw4d+5cZWXlqlWr/vCHP6jVaq/XW1paevLkyccff7yiouLw4cNKpdLn87XnaGAYxufz/fDDD+vWrRs4cKDJZII5yTBMYWFhcXExTdN33313UlISKSOdTqdGoxk1alRtbe1//vOf48ePX7161WQyRURE2O12pVLZ0NBQVFQUExOTkpISGhoKT7HH65kxY0ZNTY3Val21alVISIjL5erVq9eMGTPkcjmJdX0OFAKBQCAQCMTvmK5xNMD2FyxMJbkB+Oom8m/bIzg4eNKkSREREUlJSTk5ORUVFWfPnnU6nVC0TKfT3XvvvZMnTx46dCgYw9IPPR6PUqkkCGLx4sU7duw4evTosWPHYONOq9X27t37ySef7Nevn06nazNOe/bs2SqVasuWLV988YXJZGpsbBw8ePCjjz6qUqn69+9//PjxrKysqqqqlJQU/5+LgggbjD6fLy0tzWQyNTQ0dO/efcSIEf5eBqxlz7ODG/f5fCdPnmxubh40aFBsbOzN9V4HQ+BwOERRjIuLe/rpp6Ojo7/++uv09HTYZFYoFGFhYbNmzRo3blxCQoLb7QahRDibVqv9y1/+otfrP/nkk7Nnz+r1epvNJpfLI8IjXnjhhenTp+/Zs2fv3r3gaBB4ASdwUkbyHD9//vw1a9YcO3Zsx44dOp2O4zipDEd+fn56ejrHcWazuV+/flDhD3b1S0tLCy8W5ufn19fXv/baa7AVCVoPDz/8cExMzNatW0+fPq1QKEBoUKvVjhgxYvr06UlJSf73S+AE2IcBG/JqtdrtdoMEhv+uryiKHM+RMlKlUrlcLp1O53a7IUwAx/GqqqoPPvigoqKie/fuMTExGo3GPyPmzJkzubm5ubm5y5YtS0lJabP/fT6fXC4HuVOZTNbc3Ozz+UiClKp4sixL0zRN0fCIMQwDO9gul0sS2vS3eOGcIIKI47jb7VYqlRCsrtFoYHMb4jsEXmA5Ni4ubsmSJQsWLCgoKFi1atVLL71ksVgGDBhQVFSUk5MD9rBSqXQ6nUqlcujQoRMnThwz+tomNrStzfsiCCI4OPjNN9/s16/fN998k5GRAfZzfX19UlLSrFmzHnrooaCgIFJGKkgFjuN2u91gMHi93ubmZqgqgmGYQqG40ewJXuBJjHQ6nYWFhWq1uk+fPiaT6bqVceGtldA7oaGxwe12p6Wl6fV6qV4GyD3Ex8eTBAnRJcOHDw+IDnC5XAROqFQqqUPgMWFZlud5SkaBQcswDM/zLpeLJEmKoqqqquAAp9MpeQrAMZGQkLBy5co9e/bs3r37yJEjUBYHEsGGDBkiI2UQ/9LQ0KBUKiHMBx4WeJ+Ab4Xn+aamJkEQYFoajcb2+hPHcavVyjAMlGyAzXy5XD506NC77rorOzsbXkQQjtTc3Jyamjp16tQpU6YEOHH8aXY363S67t27r1q16oMPPsjOzq6srBRFUS6XR0dHL126dNiwYdnZ2QcPHnS73R3UTwEBhfj4+NTUVCjIStO0Uqlsbm4Gmcknn3xy8uTJ4NcQRdHpdBoMhlmzZvXu3XvHjh1ZWVnl5eXff/+9XC73eDwmkykuLm78neNDQ0NxHIeziaI4ffp0cH8cPHgQNH3Gjh07depUSJCBFw4CgUAgEAgEojNcx8TtPNJaU9qBDAhKv5WTi6IoqffzHN/sbvZ6vbW1tRzH1dbWGgwGi8ViNBpZloXaZthPwwSkq7tcLki/z83NtVqtarW6Z8+eoaGhsDytrql2u93BwcH+Mg0QXs6ybHFxcVlZmdPptFgsycnJNE2rlKrqmupn5z9bWlY6f/782bNnB6zgRUHECdxqtZpMpqqqKtip7h7VHeK6W7ezqKho1qxZgiBs3Lixf//+WEtdwLy8vMWLF1+5cuX111+/++67b64DAz4JGBG32w0bqg6Hw+PxlJaWQvh0WFhYUFAQbHJCZHibVuXly5fdbndVVRVBEEFBQRaLxWKxyOVyu91eUVGRmJgoGT9SqD8orlVXV1dWVprN5h49eoSGhmZmZr7++us1NTVvvPFGamoqRVE/CkkSRFVV1cmTJ7dt21ZeXv7hhx8GxK04nU5BEKqrq0tLS0VRDAsLMxqNUADC30KAier1esvKykiS7NmzZ8CNCIJgMBj85wBEEygUCpvN5nQ6XS6X2Wy2WCyNjY1arfbPf/5zRUXFjBkzHnroIZAw9O/tqqqqp59+2uPx3H///S+++GIHowM71dXV1XFxcdBgn89nt9shP79bt25yWg5mLahgXrhwQaFQxMXF+Y+v9IfNZrt69Wp8fLxMJlMqlXDXDofj8uXLOp0O3FUQ7AAlPzAMKy8v93q9oihGRUUpFAp4WKxWa2lpaWNjI5T9i4iIiIiIgGQi8At04GgA0x0iNS5fvlxXV+dwOHie7969u1arhQwX/0Hx+XxQPddut1dWVqpUKigt6T98AfMWnBEOh+P5558/ffr022+/fdddd4Edm5WVNWPGjOHDh7/xxhswyv6PZ3tvJJZla2trPR6P2WyWy+Ucx+l0OunlxrJsfX09OHoMBoPBYPB37oCkgtvtjo6Ohi6SvCd1dXUWi8XZQkxMjEKhAAeoKIpWq7W+vj4oKAgKx0jPCDgyQJ7Q7XbX1NRA7E9kZCRBECaTCXxAVVVVNpuNJMmYmBhQz4Geh5qvGIbV19dDHBY419rrB+jMiooKj8cTExMjNYMkyCZXk8PhaGhogKbqdLr4+Hio5gBtbrMzJcCSr62tLSkpKS0tjYyMJEkyPDwcanbU1tZC9ZbQ0NAOanbA/wV4ga+pqbl69arVaoUgFLlcDs8jRVE8x/MCD1OOpmm32w0jWFRUZLPZamtrMQyjaTokJAQkeyAIRRAE8GdBQMqVK1cqKytBESY5OTkiIsLr9d5KnWYEAoFAIBCI/0G6zNHgT5tGfled8CZonaEgxVncYvOcTueWLVs++ugjDMMOHz6s1+s7s/vqf13/VpWXl0OY7vr16/v16ydtVn/88cfvvvtucHDwzp07TSbTrTS4TToYr4CeF1tKY9xQUox/kIt/hIv/GWCVv2zZsm3btk2bNm3BggUmkwncH2C9YxgG2QHPPPNMVlbWk08++dhjj0FkuyAKkOkAdnjrk7e+Hak97d1FQHAKhmGkjIQMBelziqLee++9tWvX9u/ff/PmzU6nUyaTQRiCdAzP82+99dbOnTt7xffatXtXB130479bFPgC5DYkFcP2jLE2H5OAc/rXHej8uLfbbEHEWjQO2jxP5wnoAUEUpDFqPbj+17Lb7W63e8GCBcePH9+4ceNdE+9iOVahUKxcuXLjxo1z5859/vnnMQzjOd6/nZ1pDzTDP4nmhs5wE+A3WCCz9cGQedEljZFKtHTmrm+neEF78xY+h4IRPz7pgohh2HXH0T+srL3zI4EGBAKBQCAQiM7z866bu4ouWeHhfkAy/K2cFoJpaZru169fQkKC0+ncu3cv1r4RGECb6veg1gah8l6vl+M4giCuXr166NCh5ubmBx988Lo7h7eO+FMCvvVP97ih03bm+Lq6uoqKCgzDxo0bZzKZRFGU03KKosDLIAoiRVGghQGh9SBP4PFeJyQ+oBnSBOi8m4kgCSguCNvFEhiGQaZGSkoKQRAGgwFCAyiKko4ByUaKooxBxus27FpZRxkJ/wQMhHRA5+/3xzOTBPzT3i13PO7tnpzA21MWvBVwAoe6JFCgpL2rgDMIhD/dbrfRaCRJEvJZ3G73lStXdDodRAZhLX6KG2gDjhMkIZPJpK77ub0MwA2Nb+uDu9AYvp13fUP4v8lbfwsP5o9POkl0ZhwDnAsdnB+BQCAQCAQC0Rl+dYvI3xAMw8jl8gEDBtx///1ms/ndd9+FrOab5uKFi7BFCZKKNE3zHL9ly5bc3Nzp06c/8MADXV7L49eJ1WqFugNgEoiCyPO8IAoYhmVmZubn53s8np49e0JEvaR+d91whhuik8aGRqMhSdJms0EaPKj6g3AdwHP8d999J5PJoNjez9GG/1kYhoF0pCtXrrhcLp/PB8H8FEVZrdbs7OykpKQhQ4aAP+JGHTS/UdBsQSAQCAQCgUD8GvifWHz/TECALk3TY8eOHTlyZLdu3ZYtW4bdgpV77vw5yAaPjIzUqDUYhp2/cL6goCA5OXnmzJk6ne53rEYmCqLFYoGc7c2bNxcUFIiiyHGc5GLAMCwzM/Pjjz+urq6+44470tLSrv1QFFtXiLhtTJgwgef5AwcOZGdnV1dXi6KIE7h/aMCcuXO8Xq9Wq506deov1cjfE/4JOKD519DQkJ2dDboVERERLMsSBBEREXH06NGNGzeC8uUv3WoEAoFAIBAIBOJ/i59Fo+F/AVAmgy331nnCnefEiRObN2+Wy+U//PBDbW1tRETEvffe+9RTT3Vxc381+Gs0+ANSC2VlZTt27Pjoo490Ot3YsWOTk5MFQWhsbAS5u1OnTomiOGrUqAULFvTs2VNKq4Z0ev/Y+K6Ka+iY+vp6k8n0xBNPXL582ePxpKSkJCUlGY3G5uZmu92em5t74cIFKImybNmy5OTkX9tW88/x7N/KPXamPXB+QRBqampef/11mqarq6tzcnKioqKGDh365ptvgqsu4FT+7olfP7+2eYJAIBAIBAKBQNwoyNFw83RJ12VkZPz73/++fPmyxWIhSTI1NXX+/PntFZP/HdCeo0ESUCwrK9u+ffvZs2etVqtU4BCU7RUKxcSJEydMmKDRaAKkPX8RRwNo/rMsu3Hjxry8vJrqGmeTkyRJqF+o1+tDQ0P79+//5z//GSqn/tqi93+7jgYMwxoaGl599VWHw8EwjCAIiYmJc+bMCQkJae1l+M2BHA0IBAKBQCAQiN86yNFw83RJ19ntdqjIKJPJwsPDo6KiQIjh95ol0aajQQpMgJoOFEWVlJRA/UuopRccHKzT6aKjoz0eD47jCoXCXyW+PW6PwQZb6IIgFBYWlpaWgoqnQqGIjo6OjY2FcXS73TiO/9r8R79FR4NU5tblcpWUlFRXV+M4bjQaBw4cKBWh/K2/05CjAYFAIBAIBALxWwc5Gm6eLuk6r9fLMIzBYJDO6fP5fq9eBqxzjgZQvvD5fCAgz7IsJN7LabmUq3JDu98/Ew0NDUFBQRCn4PP5aJqGz6W6jJLp63A4tFotimjomM60x+v1gr8GwklEUQTPDkVRUk2W3/o7DTkaEAgEAoFAIBC/dZCjAfGroL2Uis7/FrjNRprkU0AgEAgEAoFAIBAIBPDr2mJFIH5bIC8DAoFAIBAIBAKBQASAIhoQCAQCgUAgEAgEAoFAdBkoogGBQCAQCAQCgUAgEAhEl4EcDQgEAoFAIBAIBAKBQCC6DORoQCAQCAQCgUAgEAgEAtFlIEcDAoFAIBAIBAKBQCAQiC4DORoQCAQCgUAgEAgEAoFAdBnI0YBAIBAIBAKBQCAQCASiy0COBgQCgUAgEAgEAoFAIBBdBnI0IBAIBAKBQCAQCAQCgegykKMBgUAgEAgEAoFAIBAIRJeBHA0IBAKBQCAQCAQCgUAgugzkaEAgEAgEAoFAIBAIBALRZSBHAwKBQCAQCAQCgUAgEIguAzkaEAgEAoFAIBAIBAKBQHQZyNGAQCAQCAQCgUAgEAgEostAjgYEAoFAIBAIBAKBQCAQXQZyNCAQCAQCgUAgEAgEAoHoMpCjAYFAIBAIBAKBQCAQCESXgRwNCAQCgUAgEAgEAoFAILoM5GhAIBAIBAKBQCAQCAQC0WUgRwMCgUAgEAgEAoFAIBCILgM5GhAIBAKBQCAQCAQCgUB0GcjRgEAgEAgEAoFAIBAIBKLLQI4GBAKBQCAQCAQCgUAgEF0GcjT8woii+Es34dcF6hAEAoFAIBAIBAKB+E2DHA0/IrZwOy8q8EInj+Q5/rrHdL7xrY/8lVj4ne8QRJt0Zp78Vvg93QvW8ob5pVuBQCAQCAQCgUD87PwmHQ0OhwPDMEEQfD5fB4e1uaYXRVEQBJZlWZaFA+ATQRD8j7ltTgeO5+AP/wa0RhRFQRQwDPN4PC6XC8MwuPf2LDGGYTq+riiKcAav13vjre56GIapq6tzu9288LuyLW8DMEudTqfH48H8ZtRvHWnOw6PR1NTkdDpZlsV+NU6xDhBFEd4w9fX10kPKsiyO479sw34mmpqapL8bGhp4ju/45YxhGMMwLMsyDCO9gmCgfT5f176BnU4nNO/XP20QCAQCgUAgfjeQS5cu/aXbcAPASrG4uHj37t0mk0mr1cpksjaP9Pl8giCQJIlhGMuymIiJgsjzPM/zgiDIZDKZTAaLfp/PB9YLQVxzu/gbAz+TYcBzvNfrpSiKZVloCezkt3c5hmHkcjmGYRRF0TTNczxFUdBmr9cLnSCKIo7jHo+HZVmlUtlxAziOw3H8s88+K4s7iEcAACAASURBVC0t7d69O0mSBEHwHC91wm3Af91/+PDhkydPyuVys9nc3pgi2kTghU2bNhUXF3fr1k2j0fA8//voQGnOW63W8+fPHz16NDs7OzMz0263BwUFqVSqX7qBHcFxXFVV1aZNm3AcN5vNcCMcx/0+hqZN4NZqamr279+fnZPdt29feP12cDyO43v27CkoKNDpdEqlEt7DNE17vV6Px0OSZMdnaA94E8LfXq/3008/LSkpCQsLU6vVN3E2BAKBQCAQCMRN8HOteiUbsssNdYEXtm7d+tlnn8XGxvbo0aPNY3iOF0WRpmn42263u91up9PZ3NxcW1srl8tVKpVcLo+OjlYqlWqVGsdxfwPA3wDufPvhV505vqmpyePxFBQUhIaGmkym3NxcQRCio6MtFkt7bYD/zM/PFwTB4/FEREQYjUYMw86ePZuTk9O3b9+hQ4fCpXNzcxUKhU6ni4qKCmief9soiqqtrV23bp1are7Xr19YWJhcLmc5lpTdzMr+1snJyfnXv/41a9as+Ph4hUJxOy/dmeHu5Hzu/BzowpNUVlWuXLmSpumUlBSz2Swjf2OmbHt9K835zZs379+/32q1RkVFNTY2KhSK559/fvr06f52Yye7q/PvpYDn5UahKKqkpGT58uXDhw9fs2YNRVFKpfI2e/FuZ/QEeFIwDCsqKlq9erXP55s4cWJERER7xwuCwPN8cXHxG2+8wfP8m2++OX78eOkkR44cKS4unjJlSlRUFEVRnb8XaXx9Ph9N0yzL1tXVrVixIiQkJDY21mQyXffnv9eQEwQCgUAgEIjbzG/PJhFEwWAwaDSaDuJgSRkJBrPX662srPz0008rKysLCwsrKytFUdRoNHq9XqPRJCcnDxkyZMSIEcHBwRC1SxAEy7LX3UYLWI/eaESuz+dbuXLl119//dJLL9177707duzIyMjo06fPunXr2jze6/XK5XKWZffu3btz506FQrF+/fqQkJDa2tovv/zyk08+uf/++xMSEnRaHS/wZ8+e/eijjyIjI1etWmU2m8HbgrVlvnq9Xp1OFxoaarfbu3XrdkO30CX49xsYBmazmed/G6kTtzgHbvoqra1xl8ulVCpNJpPdbscwjCAJnuNvv8Po5txz7eH1euFh3Lt37+7du6Oioh577DGj0ejxeJxO55gxYzp+A3SemxtHQRCu6zUgCEKlUomi2NzcrNVq5XL5ze3Pd8ztmYedp6amplu3bo2NjW63u+MjRVE0Go1yuZzneYIgKIqCXnU6nbt27dqzZw/DMPfcc09092iCvGEHjfQasdlsEREREODm/1z82voNgUAgEAgE4ndGVzoaQBeApun2FnC3bn6IgiiKItNCxwd7vd7PPvvs22+/zc3N1el0ERERCQkJSUlJjY2NFRUVDQ0Ne/bsOX369JkzZ+bNmxceHg6/khIuOm6/IAiicO0r/3WwIAhWq3Xz5s1Tp071jylgWValUjEMIwjCoUOHPv/88x49eowaNUpOy7t16yYIwpkzZ65cuRITE+P1egO29GH97fP5Tp06xXFceHh4r/heLMsSBBEdHZ2QkBAdHa3RaEDd4OGHH96/b39paenWrVvnz5/vfxcB90K0QFEUQRCiKFIyqjOjcHMwDLN3716O4+754z0YhpEyUupADMMIgsBx3OFwSPYbxFFDhsjPRHuKmDDQ153P7c2Bm5jzrd1AkiEEc0YURZIkJbeR//nBQmMYRqPRwNTlBZ71siRJ3nTv3WhEkqQwAsdfNyjjun2rUCi8Xm9xcfGBAwd8Pt9TTz01evRonU53LcsJJ3iOxwk84IpttpnneI/XQ5KkXC6XvrqVcYTd+ObmZo1Gs23rNqVKOWnSJI/Ho9Pp/C/KcRxJkhBeZDQafT6fTCbzer2UjLohNxCIHcDQtzkuXTgP/X/Y+Z9IbhdBEDQajcvlIkmy49wWgiBomm5ubuY4DrpI+oqm6V69eiUmJhqNRoPB0PpefrwuL7AcC48AvMGkryB3jCRJrVYrvegCHBZt9pv03PEcL4jCz/r+QSAQCAQCgfgd0zWOBljhQeCrKIo8x9/EHlRnEESh81tPW7Zs2bBhA47jCQkJs2bNiouL02q1er3e7Xbb7fbm5ubDhw/v379///79ZWVl//znP68bWCuB4ziO4/5KmlKrnE7n0aNHd+7cmZyc3LNnT+kAWLAyDFNbW7thw4agoKB58+Z1794dw7Dhw4fv2bPHZrOlp6dbLBaNRiMIgv8qnyRJURTPnj3rdDoFQZg4caKzyWk0GvV6/aRJkyIjI3v16oX5GQZPP/P0woULDx06NHjw4BEjRlxroSBiGIaTbRsPOI5zHPfzrap9Pl95efmmTZvi4uLGjBkTZAxiGKbNPWEph+UmGgMWIIETt7ifD/3fwXyGrm5vDtwQPMfzwjVVBZ/PRxCEpLgBl5DiyTsDmFuiKMrlco7jvF6vZIZ18CtBEKTRB8HCTkoJwA+xlnz7zvRAJ98VoigSBAHPaUxMTFhYmL8tekOQMlKj0bAs21q9os1xxNrxQEnPF8/zFEVRFNXU1PTOyndGjhx55513BjTPx/o4jgNFGPDCgFkrl8sFXgA/y3XHBZC8SwGvBekWbrT9bcJzPMiIykhZZ97eAcfD+W8xHEkURAETFArFfffdN3z48KFDh/q3v/XxBEkoZApoDMuzBP4TV0LHcT3tvcNh1DAMo2QUgRPg5aEoCqVUIBAIBAKBQNwQXeNoCFBPJGVkwA5nF9JxdQaJnJycL774ora2dubMmbNnz+7Tpw/HcQ6Hg6Iog8FgNBoJnIiJiQkKCtq6devp06ePHj06depUuVwOS39REGG/FFafBEH4r/JbBwhIEASRlZUFpoh/SQhYAet0uhUrVpSVlQ0cOPDuu+92uVwajaZPnz59+/Y9ePDgnj17pk6dimFYgM1PUZTH4zl8+HBDQ4Ner58yZYrBYIDAh/DwcIjFEEWR4zgCJ3iBHzVq1KhRo77++utdu3YNGDAA4rd5gf9ld+fq6upcLhdIykNuC+xOtwfsKErRJaIgEiTR8XSCbUv4uzPB7RiG4TjuP6Pg/P4/bHM+dzABsBYbppN+B1JGkhjZetdaOolk+YAPovN5/mAMd+ZI2F5uc+e8TcCXAb0N0qSiIPIi35nUgE6+KwReoGm6vr7e6XT26tXLYDCIohhQ+vTadjQR+NsAYCZA3EdARMB1xxHzU0iRbHWpVzMyMgRBUCqVUti//2+vmbKtzikldt0oAZv2QAe30DrLpoNeglZdm28sTxJkx4+b//Gcj/OPtYFoqU7eVJv06NEDnLD+LW99jzAfCJKAhwhrFVnTAe31W8BTI50W4lNup8oGAoFAIBAIxG+aLkudqKury8vLwzCsd+/enQ8NuFHAwun4AFhi7t27t7q6um/fvk888UTv3r2bmpo0Go3ZbG5qalIpVbDQ1+v1jz32WEFBgc1m++CDD0aOHBkSEgI2QxtnFkRp+wvH8YaGBumroKAg8ERgGFZcXPz999+Hh4W7XK6Gxh+PkclkwcHBDMOcOHHCbDYnJyeLgghZ6DRNDxs27OTJk5WVlZWVlWAU+V+aIAiGYSCiYezYsRBOTMkonuNdzS5pt00mk+EELvIijuPTpk376quvLl++/MMPPwwbNozn+V8wCZlhGJfLtW/fvsrKyuTk5PLy8qamJrPZDBkisBEa0Dyv11tXV2ez2TQajU6n0+l0oijC7iXmZ+i2tp2OHTsWHBwcHh4OYpmdQXIq/fiJKNbX13c8nwVesNlt0n8GBQVd5yrtJyM4HI5Lly5hGJaYmKjVaqXjcRyX+kEURYPBEBIS0joiHQz+gA7Mzs42GAwWi6XNQIDW/Wa328+ePYthWGpqql6v7/hewIS22WyVlZWNjY3R0dE6nU6n1XWy4EUH7wrw+4BjhcRIu93u8XhkMpkgCE6n0+VyWSwWURQ5geuk5mVJScnly5eVSmW/fv3aFBntYBw7yGcRRdFutx88eLCpqYnjOI/HA28Y6UiNWkP5gWEYZEycPHkyKCio8wEa152Hrd9FAXfXOplCuoWAOeDxeGw2W11dncPhSEpKglO1d3DA8SNHjvT/6qYNcumH9fX1ZrP5WrMFsc1QLJzADx48CPMcIrz8v+UFHhdwwR9ekFw8OI7X1dVJB0t9K+lEVFVVWa1WhmFgvIKDg/1vqmtTAhEIBAKBQCB+f9yMo0FadILC1jfffPPhhx+yLOv1ehmG4Xl+5MiRL7/8sk6na51S/nMDS8n09PTs7OzGxsY5c+Z069aN53iVUgXbX6BUL0k/Yhg2e/bs06dPl5aWrl+/ftGiRUqlEkJqd+7c+fbbb8fGxq5cuTI8PFwQBdjdAuRy+caNGzds2PDoo4/+5S9/CQ0NzcvL++ijj0BGodHa+Oabb1IUpVKpqqurJ0+evHDhQoZhtm3bRpKkWq1+5JFHCJJQqVSwAJ48efJ//vMfq9W6b9++v/71r609HUeOHKmsrHS73VOnTvV4PEqlkpSRPMfv2LFj/fr1999//7x58yAtGVbA0dHRYWFhVVVVJ06cGDZsGMTS365BCGTbtm1fffVVVVUVSZJnzpy5dOmSSqVqamqiKGrBggXj7xzv8XmwFl8MhmGbN2/+8ssv6+vrGYYxGAwsy44YMeLuu+8eMGCAFA4NAnIw3D6f7/Dhw++//35dXR2O4yRJ8jw/aNCgpUuXgu3RXnQDTGNBFPZ/vf/ll19+5pln5s6de+zYse3btxcVFUlJATRN33fffbNnz2YY5sc4dlF45plnCgoK7rzzzjfffLP1aZuamu677z673f7yyy9Pnz5d2pU9fvz466+/PmTIkMWLF+fl5b333ntFRUUMw+j1ehzHExMT//KXv0RGRqqUqq/3fb1t27ba2lqfzwfB9gMHDnz++edjYmKk0o9Yi6OB53mz2VxbW7t06dLLly9brVaFQkEQhE6nW7hw4eDBg7GWxHUQC4BGggNoy5YtjY2NUvuTkpKWLVsWGhoKETfS5y+99FJGRsbSpUtHjBhx+vTpVStX1TfUq1Qql8u1detWo9HIMVzH0QE5OTn/93//V1lZKX2uVqvHjh37+OOPy+VyaB7P8xzH7d27d//+/WfOnAkPD8/JyXnyySeh50mS/PqrrxkfI5fLA0JRQE8Uhtvlci1atKi4uBjCNIAePXo89dRTAwYMwPwMRUEUmpublyxZcubMmbVr144ePVo64bUACgI7evToqlWrGhoatm/fDsIrK1eu/OGHH3744Qeapk+cODF9+nR4+xkMhs8//xy0JKxWK0VRPM/jOP7dd9998skn+fn58CTq9XqtVrto0aLevXu73W6NRtPU1KTVal0ul1wuB8fEmjVrvvzyS4/HI7W/V69ezz//fN++fQP6tra29t57742Ojl6yZAkMtP/cJnHyiy++WLhw4fjx45csWWIymSTDeN68eXl5eYsXLx41ctQ3B77ZsGEDmNYKhYKm6R49evy///f/+vTp4y+lIU3jPXv2fPjhh1VVVTqdzul0RkZG9ujRY+HChSRJStEc183XEFuAYBN4xzocjuXLlx86dGjOnDmPPfoYTuBwF8ePH583b94jjzwyZ86cixcvbtiwwe125+fny2Qyg8GgUqkefvjhmTNnYn7RCqDp8+NVRAEXfoy2+Oabb3bs2OFwOF544YVp06bBM5WXl7dx48bysvKq6iqO4zQaDcMwZrM5NjZ25syZQ4YMaa2hg5wOCAQCgUAgEK254X0nn88nRbxTFPXaa69t2LChtrbW4XC43W6O4wRBOHr06DPPPPPDDz/c/lUXTuCCINjt9uzs7KioqCFDhly3dnp4eHh8fLxarS4sLARTp81mdxARABoKtbW1eXl5SqXSbreDcQi1LUwmE8dxer1eLpenp6c7HI7g4GCISvB4PJJOWZ8+fRwOx65du7C29gNzcnIYhunTp0///v1xHIf6nR6vx9uCfzsFXjAajcOHD2dZtqGhwWaz/bLL35KSErD3DAYDSPFjGAYuAJIkSRkJO+Eul4tl2X/84x+bNm2qqqqiadpoNMLu4vbt2xcvXrx//36sxTiBzBSCJGw227p161577bXCwkK1Wt2tWzedTsdxXGZm5tixY8vKyiD2oYPmSSPb3Nz89ddfr169+ujRo0qlMiIiQqfT1dTUWK3W9957b+3atf6RJjcaIXLN5mkRn6utrd23b9+SJUsuXrzo9XpjYmJ4nne73d9///1HH30kCMJn//1s5cqVly5dYlnWYrFAnMuRI0defPHFkydPSl4GydjmeT4/P3/e3HkZGRnFxcVxcXEqlQp22l955ZXjx49DrIHX6wUfDYZhDMMsXbp01apVDodDr9f36NEjKCiorq4uPT392Wef/f777zUajX/6T319vdvtrqysLCoqWrRoUUVlhc/ns9lsBoMBHpwO+lkUxU2bNs2dO7eqqorneYvFYrFYeJ5vaGhYvXr1nDlzysvL4UgCJ0iSXLVqVW1trdFotNlsKpXKYDDIZDKLxULTNCkjSZJsamoKuARN02CW5+XlPf300xcvXqyvrwfvFU3T5eXlp06deuKJJw4cONDm6PsjWemC2EZwU0NDw/79+202m8ViMRgMarU6LCyse/fuWq0WcltYloUULYVCwTDM0SNHlyxZkpmZSVFUTEwMhmFlZWVFRUXPPffc6dOnwZUDkSwQ/lBRUfH4449/8sknPp+P4ziz2Ww2mzmOKygomDVr1ocffuh0OtvrZ//2t3l30gyEryorKzMyMnbu2vnXv/4VMrMiIyN1Op3D4Th//vzcuXPPnj0LQq0gqIHjuMfj2bhx4/r166urq30+n1Kp7N27d01NzYkTJ5577rmioiLsp5kpN4ogCJJnzb+pPp+P5/mysrL09PRXX331woULZWVl8fHx4F+zWq1vvvnmO++8I+kEy0gZTBUJ/8yjQ4cO7dq1q6Ki4o9//OO0adMEQZDL5eXl5YsXLz5x4kR9Q73JZOrdu3d4eLhara6oqDhy5MipU6dYllUoFG63299/cRP3iEAgEAgEAvG758YiGsC+hW1DnuNXvL3i8OHDUvz/j5uEgpCVlbV69eq3335bqrxwGxZkYFL6fL7a2lqFQqFUKhUKhSiKWIdWtsFgiImJOXv2bHV1NYZhYCS0efL2zqDRaAReGDFixKBBg5xO5x133GE2m//+978PGTIERBbBtPZ4PBcuXPD5fElJSWBd0NS1DVi5XD5x4sSTJ08WFxefPn168ODB/q6B+vp6cDSMHDlSqVSCfKAgCrD3C/gfzwu8VqsdPXr0xx9/XFFRUVpa2noX1B8cxwn8Z8w9XrJkCRgna9eunTFjxksvvWQ2mx0Oh0atgfAEiCIxGAw7duxgWXbgwIGvv/66SqVSqVQEQXz//ffvvvvulStX9u7dO3ToULVaDQNEykiv1/vpp59+8sknFovlqaeeuvvuu8GQSE9PX7169dWrV1966aX//ve/nWzn7t273W533759d+3a1bNnT5qmGYYpLS1dtmzZ+fPns7KySktLo6Oj4eBb9N3k5uaePn06LCzsvffe69evn9vtZln29OnTq1at+uabb4qKihwOR+/evZ944omUlBS73W42m3fv3r1ixYq6urrjx48PHDgQRs0/6ePvf/87QRDvvPPOyJEjwQSyWq1/+9vf8vPzX3nllbVr1w4dOpTjOKVCCbbcmjVrTpw4wfP8s88+O2nSJIVC4fP5CgoKFi9efOnSpa1bt4L9DIE8oiiCq6Kurm7FihV6vf6RRx4ZNWoUxJy7XC4MwzrQaNixY8d//vMfiqLi4+OXL18OUfEwsVetWpWfn79ixYoVK1YYDUbItz958iSGYVu2bPnHP/7Ro0eP5cuXm0wmlmWhqiVoqfD8j6oQEKahVCrr6+tnz54timKP6B5r1qzp06cPTInLly+vXLny22+/XbRokdls7tevX3tNlUYWQmOwVrowJpPp4IGDBEksXLhw586d8+bNe/HFF0EcEeaMTCbzeDwcx7lcrqtXr7614q3BgwevWbMmMTERGp+Zmbls2TIYnTVr1sTGxjIMQ1GUXC4XRXH27Nk2m81kMj388MN33nlnSEgIhmG1tbUff/zx/v37V69erVFrHvjTA9dtf2cIDQ09cODA559/fs8998yfP99oNFIU5fP50tPTV6xYYbfbV6xYsX37doqiQFtUq9UeOXLk888/v3LlCsTyaDVaeIp37969evVqCNpqU5+i80CMQ8CHXq+XZdm8vLwjR44MGDBg/fr1ERERUOTCZrMtWLAgPz8/MzNzypQp8D+dNlMnmt3NOp0uJydnzZo1xcXFDz/88Ny5czEMA4fmtm3bLly4MHTo0Jdeeql3797S/wsuXrx44MCBBx98kKIoURR/jjKlCAQCgUAgEL8zbsy8JGXXSsTxHH+19OqhQ4e0Wi1If/nrjXs8HrPZfO7cue+++07abL89jgYMwwRBcDgcMpksPj4e9hg7/pUgCBaLBcT26+rqIDyB53mBFzopPMmyLCkjKYrSarVgG0Pch1wup2laSqovLCz0+XxyuTw2NhYW4s3uZofDwbIsz/FpaWmiKMbGxvrvuEIDDh86DGG9EyZMwDAMJ66ziCcJEsMwk8lkNputVmt9fX17MRq3bUdOLpc7nU6bzQbWINYS0QCA1lpdXR1BEH/6059WrlxpMpk0Gg0oRw4aNOihhx7CMKywsLCkpASW/mDZulyu1atXKxSKp59+esyYMVKNvWHDhi1YsACiVLZs2dLJcXQ6ncOGDVu3bl18XDxFUVBANC4ubsWKFTqdrqSkJCMjo6s6xOVy9ezZc8uWLcnJyVhLUsOIESNmzpzp9XorKiq6d+/+zDPP9O/fnyRJiH8ZO3bszJkzq6qqLl26VFtbC/0AqSJg2hkMhqVLl44ZPQaUHRUKhdFo/PDDDyMjIyGgAMMwjUZDykiGYYqLiy9evEgQxPvvvw9eBgzDaJpOSUlZvny5Wq0+f/785cuXeZ4H96LAC7CRu2PHDhzHV61aNWPGDNiH93q9/hkWEv7B85999pnL5Ro8ePD7778v5d6bzeY77rjj7bffjoyMvHTpUlZWFstdi5ZyOBwgvwpZHgqFAkonSienKMr/0aZpGlJd/v3vf9M0HRUVtfH9jampqdAGURRjYmLWvrt23LhxPp9v/fr1UgsxDJNqQ0Cag3/74Y/WzwgpI+12O8dxEMGE+b0baZoWeEGpVBoMBhiIAQMGPPvss3379iUJUi6X8xyflJS0adMmcGR8+umnGIbJ5XLwMmzfvr2+vl6tVi9atOihhx4KCgpiWZZl2aCgoJdeemn48OE4jn/8n4+rqqranFc3at57PB6XyzVhwoQ33njDYrFAAU6apocPH/7Iw49YLJbq6uqcnBzoBAg0+OKLLxwOR1pa2qpVq1iWZXwMfDt27NjXXnsNogk6+cTdEIIgsCxbVlaWlpa2fPnyXr16KRVKDMMYhomMjFy9ejVN0yUlJVlZWfDKhaIeFEXRLZAyUqfT5efnL1mypLS0dPr06ZCzI4k1XLp0yWg0jhkzJiEhwd/jHBsbO2/ePGnedqxii0AgEAgEAoHAbiJ1AsAJ/KuvvvL5fB6PByKx/aOmIa9ep9N9/vnn/muy2xNuWldXd+XKFZ7n9Xq9Xq/neR4ncOkf/yNh34wgiN69e2MYZrfbT5w4ERD+LW3NSRYLgPvh72RhWVapVCqVSjB7AIg/r66u1ul0crl8yJAhkJMMMocEQeAETlHUhAkTvF7vjh07ICwcVvYYhl3MvdjQ0BAfH9+rVy9YwZMkSYC/AcfB0SMZGHiLkr9arTYYDFartaampuMew/E2OqcDAgaRZVmGYfxj7AOATlapVKDpKGVQS3HpcFMajcZgMNx9991y+pozC2JnCIJITk5OTU3Nz88vKyuDPoG09nfeeYckyfj4+PHjx+t0OoZhpPSEwYMG9+zZk6Koz3d/3klpOrVaPXr0aNAKxXEc4thxHA8NCY2JiWlsbKypqZHuncCvFZKEsoWd7DoJp9N57733gjsDwzAcx/V6vUKhAD1It9s9Y8aMxMREuH3IE1EqlRMnTlQqlVartaSkRBTFgOIFYWFhgwcPhnEEFwD4L6ZNnWY2m3Nzc8FixDCMYZg9e/akp6f37ds3NjYWwpEk4uLiHnjggZqamoMHD4INJooiL/ByuRzMyLFjx3br1g1vKb0JFrLoVyxDemrg0Vi1alVTU5NarX7mmWcCniMMw+Li4pKTk5uampYtWya9LjRqDShcMgxDkqTb7b4WneRHwLXAUfjVV19hGDZ48ODW0om8wM+aNUupVGZkZBw7dgx+BR4KMOb9n1l4xiHSB9wQUpvBM0gQhFwu53lerVb7N4wgrlVXbWxsBCdmamoqVLqF6rwESWg0GovF8vDDD5eXl5eXlzudTrfbjWFYU1MTBOBMnz79D3/4A2yeS2AYdu+99/bv3z83N/fLL7+Uruj1ej0eT0AKVSfxer0qleq+++6DkQWfDoZhJElOnjK5oaGBJMmSKyUYhslksqCgoAsXLly5csVmsz355JMKhcJkMimVSnhTabXaQYMGpaWlNTY2QnBZF2Zs8RwPLmCVSjVy5EiLxYJhmCAKPMcb9Aae40NDQhMSEniel1ww0qyw2+2grsIwzMWLF1euXFlRUTF+/Pjly5cHBweLomg2m+F/WBzH2Wy2/Px8lmUhPwV0iCA+DmtxOanV6oC5h0AgEAgEAoEI4IYdDWAJ8Dyfl5fHMAwE7bfeBoTNOv9tt5swxm4OuVyu0WhIkoSVdwdhrlLGQWNjI9wFbB2DkalUKWFxKdGBo6QzrpPGxkYoVahUKsGIgl9Jynx33nmnKIrBwcFffPGFdDnYxmcY5q677vKvOAB+BljNt17sEgShVCq1Wi3Lsv56eG3euPH2dgAAIABJREFUznVb3vpOJYsLTi6Xy2+uYl8ARqORJEmCJARewIlrd0f8FKkZWq22qLBIrVYnJibCbiekZzM+BjZaTSaTz+draGyA8IfrEhISEhYWBkX7/D8XRAGMEH9lvltEp9NFRkZKyn8/XksQCIJQKBStq0u0if+cHDlypEajaf2g3TXpLjBloa4EhmEMw4AzKy4uThCE+vp6rx8cx8lkMlEUpedXFERBENxut8fjiYiImDZtGthaWIdKeIIggDvGZrM1Njb26NHDbDa3NobltBxKXfA8X19ff+23ohBwd63nauupW15e7vP5CIIYO3Zs62lP03RqaqrBYAgKCrpy5cp1etWvG2/0GZGAt2VqaqpCoWidoDRixAi73d7c3FxSUgKvqfr6eiijmJKS0rpGhtfrjYiIwDAsODjYv/0qlUqtVqvV6k7OGX9wHDebzYmJidcVi4VHr7Cw0G63h4WFQUv8v4V3TlxcHNbiYOpCwH1GEITBYEhOTgYPYEB/qtVqnue9Xq/YEtoGLVGr1eA4qK+v37Bhw5kzZ8aNGwcZExI8zzMMM2XKFJ1Od+jQoffeew8CVWia5nmeZdmbngMIBAKBQCAQ/5vcUnnLNr0M0j6SlOF824AKlCqVSq/XEwTR3NzscrmMRmN7cbwcx4GZV11dDTYtpENjLdEEkAEBG5g+n69N7YbOAKvk6upq2DUFdwaGYZJOPjBw4MDo6Oj8/PyDBw/ef//9YGNnZmaWlpZGRESMGTOmg0tIFrg0HBqNxmg0gvA+yDq0VwTkhvblcBw/fvy49J9wR2FhYdHR0ZLx2UkkO4EkSZhL4eHhFEVJXobW9yiKIiSGsCxLEmR1TTVFUUVFRe+//z6GYR6PBwwDCFmXNPMCqie0h1arhZ1S0ByRPhdFEQQC/B0WnQ8AaRMcx8PCwiAjwP9zURRlMhkI6d/oOa/pa7by+IAkoVwuByP//7d35tFRVNkff6+q9707IfseCUlAQiAEwsgyiLgB4rjgURwZz6DOjI7izHgUEJdxY0aEH+MyM6jjEdxQERGVYYQDKAYIEERDiCaELJB96726u6p+f9ykLKrTTQKJ6/2cHE+TVFe99+pV+e59936vKIput7ujo0On01VXV2/cuJHjOEXk0YkTJ8IFFx0Oh9VqNRqNvZVZZDUI+m0PZA8Fg8HW1lav15uWlmYymbxer2KSsCp2/PjxBoOhvb29vr4e5BLl/qzeaAKw/Jn+swPAqXHs2DG/36/X67OyskAbUmGuGwyG5OTkEydOSD4XAholYQ678KqQ58wZ/kHZJUA/0uVySSoq4CjxeDyjR48OP14URbvNnpiYePLkyaqqKnhHabValmXBzo+uHdAbcnLmvIUiOOH5ZfACDx9qqE2bl5fX7/zUarWgjyCerd7EYIHsPEkQtLeRDJX7g8BX5fV6oRgNJMIIgsBxHPjvVq9e/fHHHxcXF99zzz3Jycny28qyLMdxV1xxRVVV1Y4dO1599dUvv/zyklmXZGVnjRkzxmAwBINB+Sgp7ssQ9hRBEARBEOSnwaAdDRDFrVar8/LyysrKwleT0qoLDMXvUjcLqqPpdDqTycQwDIQTRzkeIqX5EN/Z2QmttdvtpE8yAExoyJ2GBOyI1xUE+cIacsvDO97Z2QnnhECJYDCoMDJhS622trarqwt2lSmlVVVVHo8HQsFBMW6Ao8GyrNlshhiKEB+CRkp/hSW4PCCcDDjB+9FHH5U+w1bh9OnTFy1adOGFFw78JHJoXzK/w+FQq9X9ehkAQRDAUtJoNFAlMRgMVlRUHDt2LBQKeb1eydHA87zdbtdoNLExsZFiOhTo9Xo4Mnxn3mw2C4IQaTopSp/K4Xme53lRFHmBZwkr9BXcMxgM/Z4NLCKtVnvWaikAxBrAlItk8FBKU1NTGxoaYMQgNMPlclksli+++KK0tFQhyN/R0ZGRkQHVFuUnOXXqlEajsVgsPM+DKl6Ui5K+V0EoFOru7lar1TqdLhgMKnbdIeTBarXGx8c3Nze3tbVBBVO5owF8DRDjEGmcweAEQx126V0ul0F/xrXArxcfH3/gwAGICBBFETxc0Hf5fYfxkd/ZfgdZ8oMo5ozYl5YlxSuRM50XMM5ZWVmQgAbPNUQ0QPFL0vdcSMeD1yYjI6OsrAwquapYFYywpAsrP394k8JvFkw2Sml41cZvX2iy6BKoK2Q0GhXX6j2SF4avpLEoiqBXEukNYzKZIDBBOh5gWdZqtW7cuHHXrl1Wq7W5ufnYsWMJ8QlwO+BsarWaoQzDMvfdd19ycnJFRcXnn3++f//+tLS08ePHl5SUzJ49W7oQpksgCIIgCIKclXOJaIBq5FddddXOnTtPnjxpNBqdTicsUqUFN+yF3nTTTdIu0HnuAA8clUqVkZERCAQ6Ojo8Hg8hhGGYfoMazGYzlEg8fvy4Vqv1eDxJSUmEELBUYdkqZRorzmAwGGCjrKenp3e7lSGiKIJEHDnTwwJnAMF8ED9nWGWpDkKIVqudPn36qlWrvF7va6+9dutvbt37+d6ysjK3233VVVexLKuQA/h2s1cm0i5ZaKCkOGTDKiMnJ0f+TzCbExISBpuxLE0JMHShI0ajMZKXgVIqlWYkhKhUKkEQIGd79uzZWq0WSg/Ijwd/jSLMW4FkkFBKwcBTbPAKguB2uxXqg4QQCMkOD9uBZrjdbilBpvf8DGWE3p3zUCgEj4yis9LWumIkKUPpGcIgvQdIngtRFCNFbYDjTLqQ5ETr6em58847x44d29LSIgndSfMH/pmclCwNptVqPXnypE6ng5aHJ0wpYBimubnZarWCLJ/H43E6nTExMfJjWBULsUKdnZ0gyhgIBsRARGH/SFMa0m38fn98fDzEz0MtGPnxoHTY2dkZHx8vT7kH25hSCoqwMBMsFgt8y+VyuVyuzs5O0vdQw1a/QphGARjk4GjQ6/WwH64YJbPZ3NTUFBsbK4oiZOX09PSANwRSgRReRXAKBIPBlpYWaRjhLRQMBsMDECA6JhAIQEIKvCKkDsLEgTnPh/jwTA2YivB+I33pJHq9Hq4IwhzSweCAFkRBXpZScb/69UorIGcqYgiiwAgM/B5CNjiOA3dw7zll/1uBhqlUKgjqYZneQA+fz6fX6/fv33/FFVeIorhu3br169er1eoZM2bIGwPSNipWdeutt7a0tJSWlu7du7esrOzNN9/cs2fPgQMH7r33XrmEbZR+IQiCIAiCIINzNIiiKPACQxlREFNSUkaOHFlZWQlWhPwYiMTOysq64YYbYBEfvuM3HEih+CkpKWazua2t7Ztvvhk3bhwJ2xsEYBPvxIkTjY2NbW1tl19+OUh/EUKgnBtYPtByxcKa4zi/32+xWHrdB6LAElYURVivg2mnuJzD4QBzurunOyYmBiLk5QeIomiz2WbOnLlv376jR4+yKrahoaGnp2f06NGxsbEsw1IaUbJRaps0zmBTqVQqlmVVrIqnvGLRf8YCvy8ufSAsW7ZMcWmHw6HQsxg4oigylDlrcRBA8qfwIT4+Ph6+FR8fP2PGDJfLBSYTHABh1bzADzZ/J3yuwkXhWvI/tbe3Q61NuK3S7yFxA34jhbX32qUMUcsYVMMitVbyNEkCBwo4jqutrbXb7ZmZmYQQvV4PKqQ+n89ut0+dOrX/c8pcWpShkUIJojzXarU6ISEBxtNgMHR3dyu8DNLlTpw4ASqqSUlJkHIlZWABDMNEr8BKKWVYJjMzc/fu3ZK+JpiO0jEgfiEIQnd3d3FxMSFEFESVSuXxeARBYFkWoi3kPYLbZDab4RUHzgI4AJ4seqYUqwQcCTZzW1tbzsgcRT4OIQQkTlwu14gRI+DxSUlJ0Wq1IA8ZLrgAnguPx6PValNTUwVB4AVeFEXIKejp6QFFSfn5odoCNF6y5AeI1KleR4PQq94KmTjhxzMsEwqEeh2vMufdwK8YBbFPf5eE+cIitp/pnULQ/WuuuebXv/51dXV1Y2Pj0aNHN27cmJubC+9hqZGUUpGIhJD4+Pj58+eXlJQ0NTW98sorBw4c2Ldv39q1a++77z4QZx2STiEIgiAIgvyEGagYpLRI7V29MZShzIoVKyZNmuT1esOtxJSUlKVLl2ZkZJDBL3DPn9TU1OnTp3u93m3btoGgXb/rXbVKHQgEduzYARbjDTfcAJ4CME7AeOvs7JQy1eWbyadOnaqrqwMzkvbJkoFBBTr2cuFASikf4hMTE8Fu6ejoIISEbyEGg0Gz2XzZZZe1t7efPHmyoqKipqbG6XROmzYtNTV1gGqLkoUWCoWampr6DLXBxRpEQRTFhISE+Lh46Qeq4p3DqSTvjyKkPNJskXuLQnyIEJKVleV2u6uqqjiOgzawKhZ+QPx/+AK5CSEmk0mlUoGSaHhTQS9AkZxCznQNRD//YJ+a6urqfn/f2dkJ4TDSVnBKSkpKSopGo6moqHC5XAoDFWyzb0eSZaJ4uM4KpTQ/P59l2erqakmoT4IP8ZTSI0eOeL1ei8WSk5Oj0Wgkk1LyMvTO4chtAEO0sLAQHDpRCpGWl5fr9frCcYWEEOgaKDWCOILinPChtbUV7HnIgpH+Kt3H8DtFKQVnpcfjaWhoYFVsuOfx0KFDECcycuRIPsQLgjBu3DiHw8Gy7M6dO1WsSjyzug0hJBgMNjY2EkIKCwtFQaSUajQatVptNBpdLpciRQgmmNfr/frrr6XuDPA9QCmFiADF7+Pj42NjY9vb28Nr2VBKeZ5vbm6WaoUOq0aP2FfYQj4+Zxwg9PojoOpwSUlJTExMSUnJjTfeGBMTc/jw4e3bt7vdbvl9AeeU9M/4+PiCgoJnnnnmV7/6VUdHx9atW4evOwiCIAiCID8xzrG8JazhLBbL6tWrr7vuOoUwWFJS0h//+Mdp06aFKxR+B8B6eu7cuVqtdteuXZ9//nmkFTarYltbW/fs2UMpnT17dkFBAUQmw16i2Wy2WCxer7ffDP/a2tqTJ096vV6XyyWZYWC0QKaGYtMvxIcgooEQ0tXVBU1SLMRhgZ6bmwvR1Lt27Wpvb+d5fsyYMRDILS9pScPCpOGDZI3zPN/R0QEVLoZj/AVRgJ/v2ItECIHIbUJIfn5+d3d3Y2NjTU2NXq9XGMxEthk7kEZGslj6PYZSOnLkSNiNV7gMurq6VCrV559/7vF4OBmBQIA7EymCpl8UESiRtvSlM1RWVvZ7wJYtW0wmE1QfhLoSoiiC5uLBgwcbGxsjVSuQT6pzmEICL0BwwZQpU3ier6mp2b9/v+I8wVCQUnrixAmO4/R6Paixyj13CqJcixCSnZ0Nz+CmTZsIIeFVQt59913IYsjKzpL22yGTwuv1trS0hJ+5tbX16NGjEKUFMQgwLPAZ9GL7TVMCoRCO4yDSRC63CWzfvl2r1WZkZKSlpfn8Po7jEhISbDZbQkLC+++/DzVW5cczDPPZZ5+dOnWK47jc3FyGZSCaTKfTQX2Z8ONFUayvr9+3b1+kcYuEKIr9unUyMzNjY2Pb2tpOnjwp/71U4kER0TDY6ypgKCM1Q54dNqgzcxzX3NwsSdWWlJRcffXVLMu+8847TU1N0f198JVLL700EAiYTKaWlhYMZ0AQBEEQBBkIA3U0yFf5sLVIGSqKYkxMzLJly5599tmHHnpo4cKFixcvfu6557Zu3Tpv3jz5WnPgBsP5IC1J7Xb7mDFjrrzySp1O9/TTTz/xxBNut5tS6vV6YcOTUhoMBr/44ovf/e535eXlarX6kUceYRgGjAFYejocjqSkpObmZtj48nq9UqRDfX39p59+WldX5/V6zWYzxDXApb1eL+wbS6UZQPNPq9UWFxeDrfLZZ5/RPgUyxSCHQiGLxXLttdfW19dv27bt4MGDhYWFo0aNIrINSTnSnp608gZnhM/nO3LkCEQfFBUVeTwehdWqPFXU7WJFI2G7G6IzVCpVeO6AArD/7XZ7fHz85s2b6+rqQCMAdPsope3t7VarFcLIw60IhjIcxzmdzm+DwAWREBIIBO64446CgoKenp7nn3++p6fHYDDwId7r9Uo3i1Kq1+ujd00URZAtkN9HOSaTSRAExRgSQqCSH6RzQ1VFQgjDMHa7ff/+/UeOHHG73SqVyuFwQNEHSH6BPXAIvBcEAWxU+cMC2hPd3d1nJC/Qb8NS5PUFXC4XbB0Hg8GGhoa3335b+gpMvFOnTu3ZvYcQcuGFF6ampoIjjGGYadOmXXzxxW1tbS+++GJ7e7vf7+c4DhoA90uyw6X/WiyWQCAQ7s2JBGUozPAZM2akpqampKQ8//zzPp8P0lt6enoIIYIgvPvOu2VlZV1dXcuXLwfTHToLf+V5HgRZw98hciDChef5uXPnEkKqq6t37typ1+v9fj/0NxQKtba2Pv/88xaLZebMmcXFxdJbSKvRms1mk8n0ySefgBol/B4CHD777LPdu3fD/rler4fwB7gLOTk5BoPh8OHDtbW1Ur8IIfCYQ6yBWq3et2/f8ePHrVYrH+KhhA0h5PDhw1u3bnU4HBMmTFCr1SaTSa/X8yH+5ptv1mq1lZWV77zzDtxf8D9CXNVrr71WU1Mzfvz4yy69TJowFoslOTnZ6XSWlpaCygOMCYTz7Nmzx+/3m81mqbYItD/EhzweD9z3YCgoNV4OiJL2TmyWYVl29OjRhYWFbW1t6/69rrq6uvflI4hwN3fv3r17927wsBiNRik4pd93PpTyMRgMcH8ZhgFxCpjMHMexLAtzXsX2prdA8ojkPVTON0rhNkm/gSFSqVQmk0nyOlmt1uuvv76goKChoeH+++93uVzSyEg3EfyA0jukrKxMr9erVKrU1NQBTn4EQRAEQZCfOecY0SAHKlBcecWVv//97xf/dvHkyZOjaKQNN9L2lN1mv+6662bPns3z/Lvvvnv33Xc/++yze/fu7enpaWxs3LZt29/+9reFCxc2NTXp9frly5fLTwJi+5mZmSUlJRaL5c0339yyZUsgEOjs7Ozu7i4vL3/44Yd37dqVlJRkt9u9Xq/cNo6Lixs5cqTL5Tp27NiuXbvAfO3u7nY6nSpWFRsb6/f7Kyoq4GCF4Qoa8mq1urCw0O/3NzQ0dHR0FBYWguci+iae2Je3DBaaVqutq6vr6OiIjY2FsoJQFfJ7wWQyOZ3OvLy8lOQUlUq1YcMGn88HdqzT6YSWg4CiWq0Ojx9hWEar1ZpMpp6eHrAWILyZUmqz2f7whz/4/f6dO3cuWrTonXfeqW+oB2O1rq7utddeW7BgwYYNG6LHdEB8e2dnZ78x8IBGowHDSf7LK664wmQyUUoffPDB7du3Q1mKrq6u9evXP/jgg36/Pzk5mWVZURStVqtGo5FSOTiO8/l8oVBIrVIrMmhAzx/sNEiJl+B53u12d3Z2Qt1Wn88XCARiYmLUajXoC1it1ieffHLVqlV1dXUwCFu3br399ttrTtQwDLN8+XJKKR/iRVFUq9V2u72wsNBms+3du/fmm2/+4IMPWlpaQqFQW1vbl19++dJLL11++eX/+c9/5A2AJClJJfGsyN8Djz76KMMwNTU1V1555YGyA+3t7SzL1tXVrV69+v/W/p/f7587d25RUZEgCGCfw3c5jnO5XB6Px+PxgPUe6Vpw42w2229+85u0tLRTp0799a9/Xb9+fWNjIx/iW1pa3n///auvvrqrq8toNN51113y77IqFirLHjx4cOXKlQcPHRQEwel0NjU1PfXUU88884zD4bBYLGDtS/aqRqPJycnRarVff/31tm3bXC6X0+V0u91QVBWUJqDAZ0tLy3333bd+/fr6hnqO47p7ut96663bbrvNYrEwDLNw4ULSV4NGEIWSkpIpU6bYbLbHHnvskUceqaysBEdSZWXl4sWLjxw5YrPZ7r33XgKiCbzAh3ij0XjppZeaTKadO3f+85//bG9vh0oW1dXV995779q1a61Wq16vB2OeEAJzAwxpeNx0Op0iM4tSCvMNJHV9Pp/b7Xa73VardebMmUVFRZXHK//yl7989NFHPp/P5/cFg8HVq1c/9thjarVaGqt+pRwkAsEAzGfwDEKbGYYxGAzg8xV646VEXuAhTUkQBHh19PucgoJveHkgqY/SSyAuLu76668fP358Y2PjqlWroJqPz+dbsGDBUyufOnz4cFdXVzAY7Orq6u7ufuONNzZs2KDT6fLy8qLMQARBEARBEETOIFLr5QkIiu1xURQhnZsQAvtp3xab+J4UuVkVO3r06Kf//vSzzz37xhtvlJeXf/XVV21tbRBpDFtnVqt17NixDzzwQFxcHHwLArZhRRsTEzNnzpwvv/yyvLz86aefXr58+YgRIzo7O00mU2xs7COPPOJ0Om+//Xa1Wq2ob/enP/2purq6s7PzzjvvdDgcWq121KhRDzzwgNFgnDVr1jfffFNTU1NbW5uRkcFxnNx2pQwlAlGpVIWFhXl5eadPn46Pj58wYYJk2jEsI0bVV5P+2tXVdejQIYvFUlRUFBsbO0yDPECcTqfRYJwyZcquol01J2p27Nixfft22G1esGDB3XffbbPZYM9fo9GEGyeUUqPRaLVa7XY7FH2EbkKFxeLi4qVLl77++utfffXVypUrQ6FQV1cXIcRut/M8z7IsiG5EcX6pVCqDwWCz2eQRDfIZ3traCrNa0TaDwXDLLbe8/fbbzc3Ny5cvf/zxx+Pj4+vr63mez8/PX7t27Z///OempiZoj4Rer7dareA6Cdfd6O7uBiVR+CD/E8uyRqPRZrMZDAaj0QjygZCko9PpMjIy1q5du2LFihdeeOGVV16BSO9AIKDVauPi4v7+97/bbDZQPxVEgVJqtVpvu+225OTkDRs2fPPNN48//rjX64WhBpFItVqdmJgobwDHcQzDcBzndrsjVbiIRFZW1oMPPvjsP56tq6+7+uqrk5OTdTodbNcnJiZOnTp1yZIlJEy4BLwnbB+RTi6KomRe2my2NWvWPProoxUVFQ8//DBs7IONrdVqk5OTV69eHX6qG2+8saKiwuPxvP/++x9++CFsm6tUKq1WO2vWrPnz57/44otVVVWiKLIsC4UhtFptfn7+vHnzPvroo3//+9/bt2/v7u7u7u7euHFjfl4+lCOJiYkZN27c4sWL16xZs2bNmieffNLhcBBC3G63Xq/PyclZtmyZwWAAyxmmK7iEenp6ysrKNm/e/NZbb8GcBBdMenr6kiVLCgoK5PNTr9dffvnlR48e/fTTT7ds2bJ+/Xp4UzEMExMTs3jx4osuuuiGG25ITk7uLYjDsODzio2NhaADeBHJXyzQfXguDAaDXq+XSt5Mnjy5vr4+EAi0trY+8MADGo0GHjFBEGbMmDFv3ryXX365urpaFEXwnEZCms8ajUav10tvwlAoxLKsXq8PhUIhPkQFCi4buMtQF0ZC3maO46Avra2tcXFxgihQsVfyNhgMgoaFWq2GgK8JEyY0NDTU1tZu3rw5ISHh7rvv9vv9aWlpH3zwwVtvvQVCv4mJiV9//bXNZmMYJisra82aNYQQr9cbKdUIQRAEQRAEkTgXDb8oBAIBlmG/s0qWUYANMZ7n77rrrsmTJ3/xxRdVVVXNzc0qlaqrq8tut+fl5WVnZ8+ZMwcMNvlWlcFgaG9vNxgM+fn5S5cu/fDDD8vLyzmOEwVxzJgxF1xwwfTp0ydMmHD8+PFp06bFxsaC+LzkiMnOzl65cuW6deukKgBxcXEGg4EydM6cOS+99FIwGDxw4EBmZma49csyLC/warV67ty5mzdv/sUvfgG1JOXZ8oqvyB1AsINKCKmvrz927Fhubu7EiRNFUQSl+kiFA4Ybi8Xi9/spQxctWmS1Wg8cOACien6ff9SoUWAbmM3muLi4uLg4hSEB6HQ6o9GYn5+flpZGCBF4AUz0YDBos9nmzZs3efLkf/zjH42NjW63G0aA47js7OwZM2ZMmTIleogN2J+5ubkg0R9+gN1uT09Pv+CCC+Dq8i/edNNNY8aMef31191ud3NTMy/wkyZNys7OXrRoUVxc3NSpU70eb2ZmZiAQgDgFk8lkMpkSEhIEQbDZbKIodnZ2gvEJmEymsWPHsiwLsRLS7yEmHNppt9sl1xjkhmRmZk6ZMiUmJua555574YUXKisr6+vrR4wYQSnNy8u76qqrkpOTCSFqlZr0JexAbP+0adMmTJjw8ssvnz59uul0UyAYcDgcfr8/Nzd37Nix06ZNk/d37NixkIxzDiFLOp1u2tRpOTk5H3/88SeffALinTqdLiEhYfbs2ZdccglYkpLJzfO8TqcbMWJEdnY2CExEuajk/YQ8FLvdvnz58n379n388cfNzc08z5vNZofDkZGRcc8995D+5BIIIQ899NDEiRN37NjR1dXl8XhYlk1PT59YNHHWJbNCoVBubm53d7fJZNJoNJDFQwjR6/XLli0zGo0H9h/gBT4lJSU9Pd1ms7EqNhgKarXa7OzsX/7yl6NyRt1///3vvfdeVVUV5LMEAoGJEycuXLjQ4XDA7dBoNHyIhyq5hJBVq1bt3Llz06ZNklSnwWCYNGnSnDlz0tLSIE5K3viYmJgVK1Z8uPXD9za/l5qayvP8qFGjtFrt/PnzJ0+e3N7ePmvWrMTERIg1kN7PFotl9OjRaWlpWq1WKq8DwCOWnJyclJQUFxcHrj3KUvAxLViwoLi4eMuWLcePH4faxhaL5aKLLpo3bx6ozLhcLqPRGEW/lg/xGo0G5rPb7ZbmM/Q0LS0tPz8/NjZW8h/Bc52YmJiTkxNJVSEtLe2CCy5IT0+HEDBJ06SoqKirqwu8KuDQDAaDFotl/vz57e3thw4dgswdk8l0xx13/Pe//y0tLYWb4vf7J02alJKSUlBQcO011xJCIPkuUqcQBEEQBEEQiX6KPkan3+MjWb/9/ul8AINt9erVr7766qpVq+bMmdPvRSMl8Q4EytDwpaTizP0iCkr5NAiiBiB0ORgMrljgLxnRAAAG80lEQVSxYvPmzcXFxf/6178Yhgn3y0BRRkqpXAiNyEZS+uD3+ze/t3nZ8mW33HLLkiVLzGazz+eDqplLliz53//+d/HFF69duxYOVhgSlNLa2trf/va3iYmJS5cuzc3NJWfGug8foihC2D9EaihaRQZQDDVKhAJU9CCEwA62VqMdYLWO86HXPhZESazzHM4AH84w9mTdPOuYnANSug0Y9lIOPEyhYZoMYOk5nU6GYaA0rKI9hBCe5+XKl/0+BSTs3SLNcPk5pXASlmU1Gg2k50Q6w6AIb7n81vMhfrATD9o/wCYpjG34IugLKGq4KL/IC5EOiDT3whFFUeAFXuClYC6YNgOfM9HPf1bggYt+Bmk+yPs1kPvS3t4OHyiloB8Bc0aesiFVDCXfX9QegiAIgiDID5khjmj4SSLZY9EPCzeHrFYrkS1D+RCvVquvueaa0tLS6urqLVu2XH/99eEabJShLIkWFSLZnBqNxulySsJphBCWZdVqdWlpaWlpaXJycnFxsfQtSUTte4dSajAYdDpduGtmgEQxMGJiYs6jaT8s5N0cDmNGMsMYhlHokg7fVNFqtVKmg1zbT06ULIlIyB0xcvNSPh8kQ3FIBjOKy4P0yYgM9oTn1jD4liBE9CDIGcgx5Gw+R0opq2LPJ0LqPN1YAxmofi9x1vsiimJMTIxCCZWElbr4gbxLEQRBEARBfrAMwaZlv2u+SErj53+tgaxQe9eIzCCKKfR+kRmaNodrrQuCwAs8IaSoqOiyyy5zuVybNm0Kr0UvNWMgV3E6nXV1dQzDgEcDguEbGxvXrFkjCML06dNBZ+4HhSS4CKUrIk2eIbyiKGMIT6uAUno+k2eYnpcBIggCFESAmgjDET0hIYpiKBSCLBWe5/stdjAkV4FOSf2C1IxhKvU6JAyqYeFvmB9sv4aJYeovDKlUzYdAyE+Ih/qpCIIgCIIgyAD58aWbfsfr6QGGM4R/S2HcQqVD+HzTTTeNGzfu6NGjmzZtOp+2nT59urGxUa/XJyUlQSi1IAh79uypqalJS0275ZZbBn4qKAF4Po0ZCJKMPPxzOCz/3iQGGUN+iZ8e8nqlkbw/QwWllOf53lB/nh/gBvs5XAU6JfVLqmSBIFGQu6jgB10MCIIgCIIg58CgUye+330zhjKE6a0KQQhxuVxy7UB5mCul38pPDDyooV+NgPPZppY+QzuhSampqU888URra+ug2iZFfb/00ksajYZhmE8++eTQoUMJCQl5eXmQV08IKSoqWrdunc1mk0sM9tsRqPtACOlXAXE4YBhGpMrd8qGdUfKAZ7mjZ7iN5+/mQj8NQJOS9E28IU9k+Dnwc+vvdwmllLKyjIlzzfBCEARBEAT5OfOj0WjoNbMZyggMx3E+n89oNPZboWCoLid3WwzhmeG0KSkpiQmJsJ07qPNTSsvLyw8fPqzX63t6emw227XXXpuWlibFhKenp1NKoYpblPOArAOU0+M4DiLYvwPdRIUHZ7hTndEe+wEiTTMMMUB+aIS/lNDLgCAIgiAIcg78aBwNAOw1paenz5w5U6fTRZIQh8XiMHkKzhPIFddoNNDyc4jtv/XWWy+88MLGxka73V5UVDR9+vRAICB1VioIF12YHcT/CgoKwN3wHbgY+uUn4whQSMchCIIgCIIgCIL8bBn2LeWhQt5OSqnb7Var1ZJRHf34gfcxPGPifMZHfjZF++EDx3EKtf/ohEIhQRAg8lzuGvD5fBq1RvoN6EGoVWqGjSZ9JwgCx3E8z0MG+5Bki/yg6HfMh/VyP41x+74Y7LOGo40MBwP5f8dP722JIAiCIAgytPxoHA1yBmtA/pANmHOQmST9LXOluvS9EpRCb6ZJpJ12ZbW2vjxk3JlHEAQBBvJ+xrclgiAIgiBIOD+y1AkkEuGLXXAc9OuUCV89D1VdTwRBEARBEARBEORnDjoafmREcgcoYxyYs++zoWcBQRAEQRAEQRAEGXJQ9R1BEARBEARBEARBkCHjR6nR8HMmXKMh0jFy+j1eWcUNAxwQBEEQBEEQBEGQ8wYjGhAEQRAEQRAEQRAEGTIwogFBEARBEARBEARBkCEDIxoQBEEQBEEQBEEQBBky0NGAIAiCIAiCIAiCIMiQgY4GBEEQBEEQBEEQBEGGDHQ0IAiCIAiCIAiCIAgyZKCjAUEQBEEQBEEQBEGQIQMdDQiCIAiCIAiCIAiCDBnoaEAQBEEQBEEQBEEQZMhARwOCIAiCIAiCIAiCIEMGOhoQBEEQBEEQBEEQBBky0NGAIAiCIAiCIAiCIMiQgY4GBEEQBEEQBEEQBEGGDHQ0IAiCIAiCIAiCIAgyZKCjAUEQBEEQBEEQBEGQIQMdDQiCIAiCIAiCIAiCDBn/D2gTsfnMDR0FAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 基于 TextRank 算法的关键词提取\n",
    "\n",
    "（1）TextRank\n",
    "\n",
    "TextRank 是另一种关键词提取算法，基于大名鼎鼎的 PageRank，其原理可参见论文—— [TextRank: Bringing Order into Texts](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf)\n",
    "\n",
    "（2）PageRank\n",
    "\n",
    "**PageRank (PR)** 是一种用于计算网页权重的算法。我们可以把所有的网页看成一个大的有向图。在此图中，节点是网页。如果网页 A 有指向网页 B 的链接，则它可以表示为从 A 到 B 的有向边。\n",
    "\n",
    "构建完整个图后，我们可以通过以下公式为网页分配权重。\n",
    "\n",
    "![cc99e37ef3794fd097ec721c8adf7809.png](attachment:cc99e37ef3794fd097ec721c8adf7809.png)\n",
    "\n",
    "（3）TextRank 原理\n",
    "\n",
    "TextRank 和 PageTank 有什么区别呢？\n",
    "\n",
    "简而言之 PageRank 用于网页排名，TextRank 用于文本排名。 PageRank 中的网页就是 TextRank 中的文本，所以基本思路是一样的。\n",
    "\n",
    "（4）基于 TextRank 算法的关键词抽取\n",
    "\n",
    "- jieba.analyse.textrank(sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v')) 直接使用，接口相同，注意默认过滤词性。\n",
    "\n",
    "- jieba.analyse.TextRank() 新建自定义 TextRank 实例\n",
    "\n",
    "（5）useful link\n",
    "\n",
    "- TextRank中文,英文关键词提取：https://blog.csdn.net/Cocktail_py/article/details/113339568"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Textbank\n",
      "银行 1.0\n",
      "起点 0.92\n",
      "反转 0.85\n",
      "处于 0.81\n",
      "季度 0.49\n"
     ]
    }
   ],
   "source": [
    "print('Textbank')\n",
    "for keyword, weight in analyse.textrank(data, withWeight=True, topK=5):\n",
    "    print('{} {}'.format(keyword, np.round(weight,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. SnowNLP\n",
    "\n",
    "SnowNLP: Simplified Chinese Text Processing\n",
    "\n",
    "SnowNLP是一个python写的类库，可以方便的处理中文文本内容，是受到了TextBlob的启发而写的，由于现在大部分的自然语言处理库基本都是针对英文的，于是写了一个方便处理中文的类库，并且和TextBlob不同的是，这里没有用NLTK，所有的算法都是自己实现的，并且自带了一些训练好的字典。\n",
    "\n",
    "注意本程序都是处理的unicode编码，所以使用时请自行decode成unicode。\n",
    "\n",
    "Git Repo [link](https://github.com/isnowfy/snownlp)\n",
    "\n",
    "安装：pip install snownlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中石化真心棒，我赚了好多钱 0.7961687156385213\n",
      "这个股票简直烂到爆 0.024879725715451606\n"
     ]
    }
   ],
   "source": [
    "from snownlp import SnowNLP # 使用\n",
    "from snownlp import seg  # 分词库\n",
    "from snownlp import sentiment # 情感分词\n",
    "from snownlp import normal #停用词处理\n",
    "text1 = '中石化真心棒，我赚了好多钱'\n",
    "text2 = '这个股票简直烂到爆'\n",
    "\n",
    "print(text1, SnowNLP(text1).sentiments)\n",
    "print(text2, SnowNLP(text2).sentiments)\n",
    "  # .sentiments：0.9769663402895832 positive的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.464 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "生物 1.0\n",
      "提出 0.82\n",
      "投资人 0.78\n",
      "公司 0.59\n",
      "管理层 0.57\n",
      "相信 0.52\n",
      "感情 0.52\n",
      "应该 0.44\n",
      "产品 0.4\n",
      "上市 0.39\n",
      "情感评分（0.6以上为积极，0.2以下为负面）： 0.09\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "    # from jieba.analyse import *\n",
    "import jieba.analyse as analyse\n",
    "    # 基于 TF-IDF 算法的关键词抽取\n",
    "text = '12月5日14时30分，在“沃森生物转让泽润生物股权”的电话会上，沃森生物董事长李云春遭投资人猛烈炮轰。除了质疑贱卖子公司，投资人还提出公司应该停牌，甚至提出向监管层举报。“你们把我们这些炒股票的当傻子吗？你看看万泰生物值多少钱，你竟然卖的那么低！你们这些人不相信因果报应吗？”“你们是主动卖泽润的还是泽润管理层逼迫你们卖的？”“泽润产品马上上市了，可以自己造血了，为什么要卖？”对此，公司管理层的回答则是——“我们主动卖的，我们是专业的，我们是对沃森倾注了感情的，请相信我们”。'\n",
    "\n",
    "for keyword, weight in analyse.textrank(text, withWeight=True, topK=10):\n",
    "    print('{} {}'.format(keyword, np.round(weight,2)))\n",
    "s = SnowNLP(text)\n",
    "print(\"情感评分（0.6以上为积极，0.2以下为负面）：\",np.round(s.sentiments,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news</th>\n",
       "      <th>sentiments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>董宇辉和新东方都回不到原点</td>\n",
       "      <td>0.756027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023年12月19日涨停板早知道：七大利好有望发酵</td>\n",
       "      <td>0.607247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12月15日沪深两市涨停分析：创指再至年内新低 龙头股份录得三连板</td>\n",
       "      <td>0.136366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>港股异动 | 来凯医药-B(02105)午后涨近10% 股价暂现五连阳 公司AKT抑制剂af...</td>\n",
       "      <td>0.984072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>国泰君安：首予腾讯音乐-SW(01698)“买入”评级 目标价40.77港元</td>\n",
       "      <td>0.998920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>海通国际：首予药明合联“优大于市”评级 目标价40.47港元</td>\n",
       "      <td>0.992657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>浦江国际盘中异动 临近午盘股价大跌6.45%报0.290港元</td>\n",
       "      <td>0.127139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>大行评级｜中银国际：重申福耀玻璃“买入”评级 目标价上调至51港元</td>\n",
       "      <td>0.990087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>龙皇集团(08493)上涨9.68%，报0.34元/股</td>\n",
       "      <td>0.040550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>北证50指数涨超2% 富恒新材涨停</td>\n",
       "      <td>0.776712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>数字认证：参股子公司版信通技术在App版权保护领域取得广泛成功应用</td>\n",
       "      <td>0.957099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>数字认证：积极布局数据资源整合利用，推出全方位数据安全解决方案</td>\n",
       "      <td>0.813908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>聚赛龙上涨5.01%，报45.72元/股</td>\n",
       "      <td>0.098449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>赛科希德上涨5.02%，报34.09元/股</td>\n",
       "      <td>0.093259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>时代出版：将持续提升和完善自身ESG管理水平</td>\n",
       "      <td>0.940160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>华图山鼎上涨5.29%，报63.28元/股</td>\n",
       "      <td>0.022112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>*ST民控下跌5.0%，报1.9元/股</td>\n",
       "      <td>0.449423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>三柏硕上涨5.26%，报20.62元/股</td>\n",
       "      <td>0.084695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>一鸣食品下跌5.1%，报14.13元/股</td>\n",
       "      <td>0.098125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>浙江震元上涨5.1%，报10.3元/股</td>\n",
       "      <td>0.019431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>中国有赞(08083)上涨5.77%，报0.11元/股</td>\n",
       "      <td>0.232895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>小黄鸭德盈盘中异动 大幅下跌5.07%</td>\n",
       "      <td>0.023116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>金力集团盘中异动 急速下跌8.62%报0.053港元</td>\n",
       "      <td>0.258845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>神通机器人教育盘中异动 股价大跌9.52%</td>\n",
       "      <td>0.452720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2023年12月18日涨停板早知道：七大利好有望发酵</td>\n",
       "      <td>0.782495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12月15日沪深两市涨停分析：南宁百货晋级5连板 音飞储存、上海建科实现4连板</td>\n",
       "      <td>0.070568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>天风证券：维持新东方“买入”评级 供给出清、需求强劲下优势显著</td>\n",
       "      <td>0.957945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>汇鸿集团下跌5.22%，报3.45元/股</td>\n",
       "      <td>0.059163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>短剧概念股再度冲高，引力传媒录得三连板</td>\n",
       "      <td>0.217691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>奥特维获国信证券买入评级，串焊机龙头，平台化布局打开持续成长空间</td>\n",
       "      <td>0.999104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>博尼控股(01906)下跌13.04%，报0.4元/股</td>\n",
       "      <td>0.072668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>润普食品上涨5.94%，报8.74元/股</td>\n",
       "      <td>0.268645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>慈文传媒9.96%涨停，总市值40.85亿元</td>\n",
       "      <td>0.690576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>海螺创业盘中异动 快速上涨5.09%报6.380港元</td>\n",
       "      <td>0.240104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2023年12月15日涨停板早知道：七大利好有望发酵</td>\n",
       "      <td>0.646062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>12月14日沪深两市涨停分析：苏州科达晋级6连板 博信股份、南宁百货、龙韵股份均走出4连板</td>\n",
       "      <td>0.251500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>多模态概念股大幅走低</td>\n",
       "      <td>0.123702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>中信出版上涨5.22%，报34.86元/股</td>\n",
       "      <td>0.175336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>朗玛信息：已将医学人工智能领域的研究和开发作为公司的战略发展方向</td>\n",
       "      <td>0.999932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>赣锋锂业上涨2.09%，报40.6元/股</td>\n",
       "      <td>0.029701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>微创光电上涨7.5%，报11.9元/股</td>\n",
       "      <td>0.115910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>上海建科下跌6.93%，报21.5元/股</td>\n",
       "      <td>0.067897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>东方盛虹：聚焦新能源新材料产业，打造全球领先的能源化工企业</td>\n",
       "      <td>0.324178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>长虹能源上涨12.09%，报17.62元/股</td>\n",
       "      <td>0.001087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>新宙邦：截至2023年9月30日，已递交并被受理的专利申请累计1050项，其中“一种锂离子电...</td>\n",
       "      <td>0.007078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 news  sentiments\n",
       "0                                       董宇辉和新东方都回不到原点    0.756027\n",
       "1                          2023年12月19日涨停板早知道：七大利好有望发酵    0.607247\n",
       "2                   12月15日沪深两市涨停分析：创指再至年内新低 龙头股份录得三连板    0.136366\n",
       "3   港股异动 | 来凯医药-B(02105)午后涨近10% 股价暂现五连阳 公司AKT抑制剂af...    0.984072\n",
       "4              国泰君安：首予腾讯音乐-SW(01698)“买入”评级 目标价40.77港元    0.998920\n",
       "5                      海通国际：首予药明合联“优大于市”评级 目标价40.47港元    0.992657\n",
       "6                      浦江国际盘中异动 临近午盘股价大跌6.45%报0.290港元    0.127139\n",
       "7                   大行评级｜中银国际：重申福耀玻璃“买入”评级 目标价上调至51港元    0.990087\n",
       "8                         龙皇集团(08493)上涨9.68%，报0.34元/股    0.040550\n",
       "9                                   北证50指数涨超2% 富恒新材涨停    0.776712\n",
       "10                  数字认证：参股子公司版信通技术在App版权保护领域取得广泛成功应用    0.957099\n",
       "11                    数字认证：积极布局数据资源整合利用，推出全方位数据安全解决方案    0.813908\n",
       "12                               聚赛龙上涨5.01%，报45.72元/股    0.098449\n",
       "13                              赛科希德上涨5.02%，报34.09元/股    0.093259\n",
       "14                             时代出版：将持续提升和完善自身ESG管理水平    0.940160\n",
       "15                              华图山鼎上涨5.29%，报63.28元/股    0.022112\n",
       "16                                *ST民控下跌5.0%，报1.9元/股    0.449423\n",
       "17                               三柏硕上涨5.26%，报20.62元/股    0.084695\n",
       "18                               一鸣食品下跌5.1%，报14.13元/股    0.098125\n",
       "19                                浙江震元上涨5.1%，报10.3元/股    0.019431\n",
       "20                        中国有赞(08083)上涨5.77%，报0.11元/股    0.232895\n",
       "21                                小黄鸭德盈盘中异动 大幅下跌5.07%    0.023116\n",
       "22                         金力集团盘中异动 急速下跌8.62%报0.053港元    0.258845\n",
       "23                              神通机器人教育盘中异动 股价大跌9.52%    0.452720\n",
       "24                         2023年12月18日涨停板早知道：七大利好有望发酵    0.782495\n",
       "25            12月15日沪深两市涨停分析：南宁百货晋级5连板 音飞储存、上海建科实现4连板    0.070568\n",
       "26                    天风证券：维持新东方“买入”评级 供给出清、需求强劲下优势显著    0.957945\n",
       "27                               汇鸿集团下跌5.22%，报3.45元/股    0.059163\n",
       "28                                短剧概念股再度冲高，引力传媒录得三连板    0.217691\n",
       "29                   奥特维获国信证券买入评级，串焊机龙头，平台化布局打开持续成长空间    0.999104\n",
       "30                        博尼控股(01906)下跌13.04%，报0.4元/股    0.072668\n",
       "31                               润普食品上涨5.94%，报8.74元/股    0.268645\n",
       "32                             慈文传媒9.96%涨停，总市值40.85亿元    0.690576\n",
       "33                         海螺创业盘中异动 快速上涨5.09%报6.380港元    0.240104\n",
       "34                         2023年12月15日涨停板早知道：七大利好有望发酵    0.646062\n",
       "35      12月14日沪深两市涨停分析：苏州科达晋级6连板 博信股份、南宁百货、龙韵股份均走出4连板    0.251500\n",
       "36                                         多模态概念股大幅走低    0.123702\n",
       "37                              中信出版上涨5.22%，报34.86元/股    0.175336\n",
       "38                   朗玛信息：已将医学人工智能领域的研究和开发作为公司的战略发展方向    0.999932\n",
       "39                               赣锋锂业上涨2.09%，报40.6元/股    0.029701\n",
       "40                                微创光电上涨7.5%，报11.9元/股    0.115910\n",
       "41                               上海建科下跌6.93%，报21.5元/股    0.067897\n",
       "42                      东方盛虹：聚焦新能源新材料产业，打造全球领先的能源化工企业    0.324178\n",
       "43                             长虹能源上涨12.09%，报17.62元/股    0.001087\n",
       "44  新宙邦：截至2023年9月30日，已递交并被受理的专利申请累计1050项，其中“一种锂离子电...    0.007078"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests                  # 获取一个网页。\n",
    "from bs4 import  BeautifulSoup  # BeautifulSoup库最主要的功能就是从网页爬取我们需要的数据。\n",
    "import re                       # 通过正则表达式是用来匹配处理字符串的\n",
    "import numpy as np              # Numpy（Numerical Python的简称）是高性能科学计算和数据分析的基础包，其提供了矩阵运算的功能。\n",
    "import pandas as pd             # Pandas 名字衍生自术语 “panel data”（面板数据）和 “Python data analysis”（Python 数据分析）。\n",
    "                                 # Pandas 一个强大的分析结构化数据的工具集，基础是 Numpy（提供高性能的矩阵运算）。\n",
    "\n",
    "def request_url(url):\n",
    "    user_agent = 'User-Agent:Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'\n",
    "    headers = {'User-Agent': user_agent}     \n",
    "    res = requests.get(url,headers=headers)\n",
    "    res.encoding = 'utf-8'\n",
    "    return res.text\n",
    "\n",
    "url = 'https://finance.sina.com.cn/roll/index.d.html?cid=56588&page=1'\n",
    "soup = BeautifulSoup(request_url(url), 'lxml')\n",
    "\n",
    "info = [inf.text for inf in soup.find_all('a', target = '_blank')][2:]\n",
    "  # inf.text：把其中的文本读出来。\n",
    "  # [2:]：从第2开始读取。目的是删除掉文本中的'财经博客', '股票博客'。\n",
    "\n",
    "senti = [SnowNLP(text).sentiments for text in info]\n",
    "  # 把上面的各个新闻的情绪值读出来。\n",
    "df = pd.DataFrame({'news': info, 'sentiments' : senti})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "情感评分（0.6以上为积极，0.2以下为负面）： 0.4\n"
     ]
    }
   ],
   "source": [
    "print(\"情感评分（0.6以上为积极，0.2以下为负面）：\",np.round(np.mean(senti),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. 练习 \n",
    "\n",
    "通过分析新浪的新闻来判读舆情方向，并判断预测的准确程度。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from snownlp import SnowNLP # 使用\n",
    "import pandas as pd\n",
    "# Windows\n",
    "# sina_news = pd.read_csv('sina_fin_news.csv', encoding = 'ansi') - Windows\n",
    "# Mac 上修改为使用 GB18030 的编码来读取文件：\n",
    "#     sina_news = pd.read_csv('sina_fin_news.csv', encoding='gb18030', on_bad_lines='skip')\n",
    "  # F:\\hongdou\\1111111111111111课程\\Python\\python_course-master\\ProgramSession\n",
    "sina_news = pd.read_csv(r'F:\\hongdou\\1111111111111111课程\\Python\\python_course-master\\ProgramSession\\sina_fin_news.csv', encoding = 'ansi')\n",
    "sina_news['date'] = [x.split(' ')[0] for x in sina_news['date']]\n",
    "  # spilt()和split(' ')都是按照字符间的空格进行分割。\n",
    "  #  （1）split()会把多个空格当成一个空格进行分割。\n",
    "  #  （2）split(' ')会把字符中间的多个空格当成多个空格分割。\n",
    "sina_news['status'] = [SnowNLP(text).sentiments for text in sina_news['news']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_news = sina_news.groupby('date')['status'].transform('mean')\n",
    "sina_news['sentiment'] = ['Pos' if s_value > 0.6 else ('Neg' if s_value < 0.2 else 'None') for s_value in s_news]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_news = sina_news.groupby('date')['status'].transform('mean')\n",
    "  # group.transform('mean')：transform可以产生mean，并广播到各分组的尺寸数据中。获得每天的情绪均值。\n",
    "  # groupby分组。基本格式：data.groupby([‘分组字段’])。\n",
    "  #   按照date分组。\n",
    "  #   多类分组：data.groupby(['班级','科目'])\n",
    "sina_news['sentiment'] = ['Pos' if s_value > 0.6 else ('Neg' if s_value < 0.2 else 'None') for s_value in s_news]\n",
    "  # 按照每天的情绪均值来判断:pos,neg and none.整体上这一天的新闻呈现怎么样的情绪状态。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>news</th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99</td>\n",
       "      <td>英国4月零售物价指数年率+2.5%，预期+2.6%，前值+2.5%；月率+0.4%，预期+0...</td>\n",
       "      <td>2014/5/20</td>\n",
       "      <td>0.069470</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "      <td>英国4月核心CPI年率+2.0%，创2013年9月以来最大升幅，预期+1.8%，前值+1.6...</td>\n",
       "      <td>2014/5/20</td>\n",
       "      <td>0.795471</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>英国3月DCLG房价指数年率+8.0%，预期+9.6%，前值+9.1%。</td>\n",
       "      <td>2014/5/20</td>\n",
       "      <td>0.541969</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96</td>\n",
       "      <td>英国4月生产者输入物价指数月率-1.1%，预期-0.2%，前值-0.4%；年率-5.5%，预...</td>\n",
       "      <td>2014/5/20</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>95</td>\n",
       "      <td>据世界黄金协会最新黄金需求趋势报告，一季度金条金币需求同比重挫39%至283吨，为四年来最低水平。</td>\n",
       "      <td>2014/5/20</td>\n",
       "      <td>0.999945</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>4</td>\n",
       "      <td>在圣彼得堡国际经济论坛上，道达尔CEO马哲睿(christophe de Margerie)...</td>\n",
       "      <td>2014/5/25</td>\n",
       "      <td>0.823601</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>3</td>\n",
       "      <td>雷石东(Sumner Redstone)过去约一周中出售2.36亿美元维亚康姆(VIA)和C...</td>\n",
       "      <td>2014/5/25</td>\n",
       "      <td>0.675677</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2</td>\n",
       "      <td>苹果谋求禁止销售三星9款较老机型。此前苹果(AAPL)在美赢得一桩诉讼，裁决认定三星侵犯苹果...</td>\n",
       "      <td>2014/5/25</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "      <td>郑商所期货与衍生品部总监左宏亮25日在“第九届中国期货暨衍生品市场论坛”上表示，从目前的郑商...</td>\n",
       "      <td>2014/5/25</td>\n",
       "      <td>0.892710</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0</td>\n",
       "      <td>中金所期货小组成员刘炜亮25日在“第九届中国期货暨衍生品市场论坛”上表示，做市商不是期权推出...</td>\n",
       "      <td>2014/5/25</td>\n",
       "      <td>0.606726</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                               news       date  \\\n",
       "0    99  英国4月零售物价指数年率+2.5%，预期+2.6%，前值+2.5%；月率+0.4%，预期+0...  2014/5/20   \n",
       "1    98  英国4月核心CPI年率+2.0%，创2013年9月以来最大升幅，预期+1.8%，前值+1.6...  2014/5/20   \n",
       "2    97               英国3月DCLG房价指数年率+8.0%，预期+9.6%，前值+9.1%。  2014/5/20   \n",
       "3    96  英国4月生产者输入物价指数月率-1.1%，预期-0.2%，前值-0.4%；年率-5.5%，预...  2014/5/20   \n",
       "4    95  据世界黄金协会最新黄金需求趋势报告，一季度金条金币需求同比重挫39%至283吨，为四年来最低水平。  2014/5/20   \n",
       "..   ..                                                ...        ...   \n",
       "995   4  在圣彼得堡国际经济论坛上，道达尔CEO马哲睿(christophe de Margerie)...  2014/5/25   \n",
       "996   3  雷石东(Sumner Redstone)过去约一周中出售2.36亿美元维亚康姆(VIA)和C...  2014/5/25   \n",
       "997   2  苹果谋求禁止销售三星9款较老机型。此前苹果(AAPL)在美赢得一桩诉讼，裁决认定三星侵犯苹果...  2014/5/25   \n",
       "998   1  郑商所期货与衍生品部总监左宏亮25日在“第九届中国期货暨衍生品市场论坛”上表示，从目前的郑商...  2014/5/25   \n",
       "999   0  中金所期货小组成员刘炜亮25日在“第九届中国期货暨衍生品市场论坛”上表示，做市商不是期权推出...  2014/5/25   \n",
       "\n",
       "       status sentiment  \n",
       "0    0.069470      None  \n",
       "1    0.795471      None  \n",
       "2    0.541969      None  \n",
       "3    0.057408      None  \n",
       "4    0.999945      None  \n",
       "..        ...       ...  \n",
       "995  0.823601       Pos  \n",
       "996  0.675677       Pos  \n",
       "997  0.999985       Pos  \n",
       "998  0.892710       Pos  \n",
       "999  0.606726       Pos  \n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sina_news"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. 读取中文年报"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）MuPDF\n",
    "\n",
    "MuPDF 是一个轻量级的 PDF、XPS和电子书查看器。MuPDF 由软件库、命令行工具和各种平台的查看器组成。\n",
    "\n",
    "（2）PyMuPDF\n",
    "\n",
    "PyMuPDF是支持MuPDF的Python绑定。\n",
    "\n",
    "使用PyMuPDF，你可以访问扩展名为“.pdf”、“.xps”、“.oxps”、“.cbz”、“.fb2”或“.epub”。此外，大约10种流行的图像格式也可以像文档一样处理:“.png”，“.jpg”，“.bmp”，“.tiff”等。\n",
    "\n",
    "原文链接：https://blog.csdn.net/ling620/article/details/120035699\n",
    "\n",
    "（3）Document的方法和属性\n",
    "\n",
    "- Document.page_count：页数 (int)\n",
    "- Document.metadata：元数据 (dict)\n",
    "- Document.get_toc()：获取目录 (list)\n",
    "- Document.load_page()：读取页面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 安装\n",
    "pip install PyMuPDF\n",
    "# 如果上述code报错，则执行以下：\n",
    "pip install PyMuPDF==1.18.0\n",
    "pip uninstall PyMuPDF\n",
    "pip install PyMuPDF==1.19.0 -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting PyMuPDF==1.19.0\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4e/4a/2122ae22f2d34b6485a29a6ca476458888d04bb9ca1c76291b23333f291d/PyMuPDF-1.19.0-cp39-cp39-win_amd64.whl (6.3 MB)\n",
      "Installing collected packages: PyMuPDF\n",
      "Successfully installed PyMuPDF-1.19.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install PyMuPDF==1.19.0 -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PyMuPDF 1.19.0: Python bindings for the MuPDF 1.19.0 library.\n",
      "Version date: 2021-10-16 07:31:46.\n",
      "Built for Python 3.9 on win32 (64-bit).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import fitz\n",
    "    # 导入库，名字叫fitz\n",
    "    #   关于命名fitz的说明\n",
    "print(fitz.__doc__)\n",
    "    # 查看 fitz 版本\n",
    "def extract_text_from_pdf(pdf_path):\n",
    "    text = \"\"\n",
    "    try:\n",
    "        # Open the PDF file\n",
    "        with fitz.open(pdf_path) as pdf_document:\n",
    "            # 打开文档\n",
    "            for page_number in range(pdf_document.page_count):\n",
    "                # for：Iterate through pages，迭代（iterate）\n",
    "                # Document.page_count：页数 (int)\n",
    "                #               Get the page\n",
    "                page = pdf_document[page_number]\n",
    "                # Extract text from the page\n",
    "                text += page.get_text()\n",
    "                # （1）复合赋值：是指先执行运算符指定的运算，\n",
    "                #               然后再将运算结果存储到运算符左边操作数指定的变量中。\n",
    "                #               下表列出了“+=、-=、*=、/=、%=”复合赋值运算符的描述及例子。\n",
    "                #      +=：     先将运算符左边操作数指向的变量值和右边的操作数执行相加操作，\n",
    "                #               然后再将相加的结果赋值给左边的操作数指向的变量。\n",
    "                # （2）提取文本和图像：text = page.get_text(opt)\n",
    "                # https://blog.csdn.net/qq_38911076/article/details/114750826\n",
    "    except Exception as e:\n",
    "        print(f\"Error extracting text: {str(e)}\")\n",
    "    return text\n",
    "\n",
    "# Example usage\n",
    "pdf_path = \"20230627-000702.SZ.PDF\"\n",
    "extracted_text = extract_text_from_pdf(pdf_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /var/folders/v3/q74bdmsj1jn7mhzr43d3sjzm0000gn/T/jieba.cache\n",
      "Loading model cost 0.426 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords: ['正虹', '2022', '公司', '000.00', '湖南', '有限公司', '其他', '年度报告', '科技', '资产', '适用', '12', '股份', '全文', '负债', '饲料', '发展', '公允', '计量', '金额']\n"
     ]
    }
   ],
   "source": [
    "import jieba.analyse\n",
    "\n",
    "def extract_keywords(text, top_k=20):\n",
    "    try:\n",
    "        # Use TF-IDF algorithm to extract keywords\n",
    "        keywords = jieba.analyse.extract_tags(text, topK=top_k)\n",
    "        return keywords\n",
    "    except Exception as e:\n",
    "        print(f\"Error extracting keywords: {str(e)}\")\n",
    "        return []\n",
    "\n",
    "# Example usage\n",
    "\n",
    "keywords = extract_keywords(extracted_text)\n",
    "print(\"Keywords:\", keywords)"
   ]
  }
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